Wellesley College

Purpose of Review This review looks at current trends in the effect of social media use on mental health, identity development, and civic engagement for LGBTQ + youth during the post-pandemic period, when online engagement has significantly increased. It explores both risks and benefits associated with this shift and offers recommendations for clinicians and future research in this evolving landscape. Recent Findings There is an intricate relationship between the harms and benefits of social media use and developing adolescents. While some research suggests that social media use and overuse is associated with negative outcomes such as depression and anxiety, many recent studies have found that, for LGBTQ + youth in particular, social media may be a necessary safe space they have for representation and community building, especially when absent in their physical world. It is important to look at the nuances behind social media use motivations and adolescents’ intersectional identities when understanding and developing personalized interventions for mental health. Summary The review looks at how LGBTQ + youth have used social media since the beginning of the COVID-19 pandemic to increase their own agency and build community through developing their own safe spaces online. LGBTQ + youth’s hidden and intersectional identities might isolate them within their home and community environments, which must be considered when thinking about controlling adolescent social media use. Ongoing research should look into the relationship between social media use and LGBTQ + adolescent mental health at a more granular level, rather than just LGBTQ + vs. heterosexual peers to further tailored interventions.

Characterizing the dynamics of microbial community succession in the infant gut microbiome is crucial for understanding child health and development, but no normative model currently exists. Here, we estimate child age using gut microbial taxonomic relative abundances from metagenomes, with high temporal resolution (±3 months) for the first 1.5 years of life. Using 3154 samples from 1827 infants across 12 countries, we trained a random forest model, achieving a root mean square error of 2.56 months. We identified key taxonomic predictors of age, including declines in Bifidobacterium spp. and increases in Faecalibacterium prausnitzii and Lachnospiraceae. Microbial succession patterns are conserved across infants from diverse human populations, suggesting universal developmental trajectories. Functional analysis confirmed trends in key microbial genes involved in feeding transitions and dietary exposures. This model provides a normative benchmark of “microbiome age” for assessing early gut maturation that may be used alongside other measures of child development.

Thomas Schelling argued that “The most spectacular event of the past half century is one that did not occur. We have enjoyed sixty years without nuclear weapons exploded in anger.” To this, he added a question: “Can we make it through another half dozen decades?” Contemporary technological innovation, weapons proliferation, increased modernization efforts, and nuclear saber-rattling have made Schelling's question an urgent one. Recently, there has been an explosion in scholarship attempting to test the resilience of nonuse. These scholars have focused primarily on methodological innovations, generating an impressive body of evidence about the future of nonuse. Yet we argue that this literature is theoretically problematic: it reduces mechanisms of nuclear nonuse to a “rationalist” versus “normative” dichotomy which obscures the distinct pathways to nuclear (non)use within each theoretical framework. With rationalist theories, the current literature commits the sin of conflation, treating what should be distinct mechanisms—cost and credibility—as a single causal story. With normative theories, scholars have committed a sin of omission, treating norms as structural and overlooking mechanisms of norm contestation. We show that teasing out these different causal pathways reveals radically different expectations about the future of nonuse, especially in a world of precision nuclear weapons.

Research on neighborhood social organization and crime typically conceptualizes neighborhood change on the order of decades, even though the local social contexts that individuals experience change daily through mobility for work, errands and recreation. In this study, the authors analyze data from the Seattle Neighborhoods and Crime Survey linked to the Census Transportation Planning Products to investigate whether within-day changes in neighborhood diversity are associated with an individual’s social cohesion and fear of crime. The authors find that individuals living in neighborhoods where diversity increases during the daytime tend to report more social cohesion and relatively less fear of crime. Importantly, these relationships are observed only among white respondents, with implications for whether processes of racialization in diverse neighborhood contexts account for this tendency. Results from this study highlight how the “mobility turn” within theories about neighborhood effects would benefit from considering how the contexts themselves change throughout the day.

The correlational structure of brain activity dynamics in the absence of stimuli or behavior is often taken to reveal intrinsic properties of neural function. To test the limits of this assumption, we analyzed peripheral contributions to resting state activity measured by fMRI in unanesthetized, chemically immobilized male rats that emulate human neuroimaging conditions. We find that perturbation of somatosensory input channels modifies correlation strengths that relate somatosensory areas both to one another and to higher-order brain regions, despite the absence of ostensible stimuli or movements. Resting state effects are mediated by the same peripheral and thalamic structures that relay responses to overt sensory stimuli. The impact of basal peripheral input is reduced in a rat model of autism, which displays both lower somatosensory functional connectivity and insensitivity to vibrissa inactivation. These results demonstrate the influence of extrinsic influences on resting state brain phenotypes in health and disease.

Novel technologies are emerging and evolving at such a rapid pace that it is difficult for companies and society to absorb them. Large mature organizations can be displaced if they fail to learn about, develop, and adopt new technologies, yet they struggle to do so. What is the best approach? Clearly there is no single best answer. This paper examines organizational models that companies have experimented with for leveraging technological discoveries and inventions to create strategic innovations that fuel new growth opportunities. I adopt Kanter's concept of newstreams as the guiding lens, because it addresses the challenges that mature firms face in their attempts to create new platforms of growth that emerging technologies enable, while maintaining the health of the mainstream core business. This notion demands an extension of ambidexterity theory beyond the exploration/exploitation dichotomy, recognizing that creating new streams of growth that ultimately become part of the mainstream organization requires elements of exploitation to enhance reliability and predictability that the mainstream requires. Five organizational approaches for SI that have been observed in practice are described and considered in light of three elements that, together, can be thought of as comprising a technological innovation strategy: (a) type of ambidextrous approach the firm adopts, (b) type of technology (general vs. special purpose), and (c) targeted market (internal vs. external). By combining theory and observation, configurations of ambidexterity type, technology type, and target market are proposed, as well as expected outcomes for each. I offer these as a research agenda whose outcome can provide important guidance to organizational leaders who are attempting to build capabilities for technological innovation that will secure their organizations' future health.

Background Machine learning (ML) is increasingly used in population and public health to support epidemiological studies, surveillance, and evaluation. Our objective was to conduct a scoping review to identify studies that use ML in population health, with a focus on its use in non-communicable diseases (NCDs). We also examine potential algorithmic biases in model design, training, and implementation, as well as efforts to mitigate these biases. Methods We searched the peer-reviewed, indexed literature using Medline, Embase, Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews, CINAHL, Scopus, ACM Digital Library, Inspec, Web of Science’s Science Citation Index, Social Sciences Citation Index, and the Emerging Sources Citation Index, up to March 2022. Results The search identified 27 310 studies and 65 were included. Study aims were separated into algorithm comparison (n = 13, 20%) or disease modelling for population-health-related outputs (n = 52, 80%). We extracted data on NCD type, data sources, technical approach, possible algorithmic bias, and jurisdiction. Type 2 diabetes was the most studied NCD. The most common use of ML was for risk modeling. Mitigating bias was not extensively addressed, with most methods focused on mitigating sex-related bias. Conclusion This review examines current applications of ML in NCDs, highlighting potential biases and strategies for mitigation. Future research should focus on communicable diseases and the transferability of ML models in low and middle-income settings. Our findings can guide the development of guidelines for the equitable use of ML to improve population health outcomes.

Immune reactions to medical implants often lead to encapsulation by fibrotic tissue and impaired device function. This process is thought to initiate by protein adsorption, which enables immune cells to attach and mount an inflammatory response. Previously, several antifibrotic materials have been either designed to reduce protein adsorption or discovered via high‐throughput screens (HTS) to favorably regulate inflammation. The present work introduces antifouling immunomodulatory (AIM) copolymer coatings, which combine both strategies to effectively enhance implant protection. AIM copolymers synergistically integrate zwitterionic moieties to resist protein fouling, HTS‐derived antifibrotics for immunomodulation, and silane monomers for grafting to diverse substrates including elastomers, ceramics, and metals. Interestingly, simply combining these monomers into conventional random or block copolymer architectures yielded no significant advantage over homopolymers. By contrast, an unusual polymer chain architecture — a zwitterionic block flanked by a mixed zwitterionic immunomodulatory segment — showed superior fibrosis resistance in both peritoneal and subcutaneous sites over one month in immunocompetent mice. This architecture also improved the performance of two different HTS‐derived antifibrotic monomers, suggesting that tailoring AIM architectures may broadly complement immunomodulatory chemistries and provide a versatile approach to improving implant longevity.

Matching is one of the most fundamental and broadly applicable problems across many domains. In these diverse real-world applications, there is often a degree of uncertainty in the input, which has led to the study of stochastic matching models. Here, each edge in the graph has a known, independent probability of existing derived from some prediction. Algorithms must probe edges to determine existence and match them irrevocably if they exist. Further, each vertex may have a patience constraint denoting how many of its neighboring edges can be probed. We present new ordered contention resolution schemes yielding improved approximation guarantees for some of the foundational problems studied in this area. For stochastic matching with patience constraints in general graphs, we provide a 0.382-approximate algorithm, significantly improving over the previous best 0.31-approximation. When the vertices do not have patience constraints, we describe a 0.432-approximate random order probing algorithm with several corollaries, such as an improved guarantee for the Prophet Secretary problem under Edge Arrivals. Finally, for the special case of bipartite graphs with unit patience constraints on one of the partitions, we show a 0.632-approximate algorithm that improves on a recent result providing a guarantee of 1/3. Funding: N. Grammel and A. Srinivasan were financially supported in part by the National Science Foundation Division of Computing and Communication Foundations [Award CCF-1749864] and by research awards from Amazon and Google.

We present the genomes of nine cultured microbes isolated from two freshwater sites in Wellesley, MA. The dataset is useful for exploring genomic diversity among freshwater taxa, including Pedobacter , Pseudomonas , Rhodoferax , Rouxiella, and Flavobacterium .

Efficient and accurate calculation of macromolecule pairwise similarity is essential for developing database search engines and is useful for machine learning based predictive tools. Existing methods for calculating macromolecular similarity suffer from significant drawbacks. Graph edit distance is accurate but computationally expensive, and graph kernel methods are computationally efficient but inaccurate. This study introduces a graph neural network model, MacroSimGNN, which significantly improves computational efficiency while maintaining high accuracy on macromolecule pairwise similarity. Furthermore, this approach enables feature embeddings based on macromolecular similarities to a set of landmark molecules, enhancing both unsupervised and supervised learning tasks. This method represents a significant advancement in macromolecular cheminformatics, paving the way for the development of advanced search engines and data-driven design of macromolecules.

The present study evaluated the inter‐rater reliability of the Heads Up Checkup (HCU), a brief digital mental health and behavioral adaptive screening system designed for use in primary care and diverse school settings. Two independent licensed clinical psychologists reviewed a random sample of 30 (N = 30) HCU clinical screening reports of 13−14 year old adolescents drawn from a larger sample (N = 846) enrolled in a public middle school in California. Results showed strong inter‐rater agreement (Fleiss kappa = 0.93) between clinician ratings and the screener's priority risk index (HPI) in identifying students “in crisis.” In addition, clinicians' ratings of confidence in their priority judgments were found to be significantly higher for the “in crisis” cases. Reasonable evidence of convergent validity emerged due to a strong relationship between clinician ratings of psychological distress and the HPI. Overall findings suggest that as an online universal school‐based screener, the HCU has valid utility for identifying young adolescents “in crisis” which can translate into timely interventions and pragmatic real‐world therapeutic solutions. Future research directions with respect to the refinement of the HCU's measurement characteristics and its feasibility as an online screener at the population‐level in schools are discussed.

In aquatic ecosystems, allochthonous nutrient transport to the euphotic zone is an important process that fuels new production. Here, we use high‐resolution physical and biogeochemical observations from five summers to estimate the mean vertical nitrate flux, and thus new production over the Northeast U.S. Shelf (NES). We find that the summertime nitrate field is primarily controlled by biological uptake and physical advection–diffusion processes, above and below the 1% light level depth, respectively. We estimate the vertical nitrate flux to be 8.2 ± 5.3 × 10⁻⁶ mmol N m⁻² s⁻¹ for the mid‐shelf and 12.6 ± 8.6 × 10⁻⁶ mmol N m⁻² s⁻¹ for the outer shelf. Furthermore, we show that the new production to total primary production ratio (i.e., the f‐ratio), consistently ranges between 10% and 15% under summer conditions on the NES. Two independent approaches—nitrate flux‐based new production and O2/Ar‐based net community production—corroborate the robustness of the f‐ratio estimation. Since ~ 85% of the total primary production is fueled by recycled nutrients over sufficiently broad spatial and temporal scales, less than 15% of the organic matter produced in summer is available for export from the NES euphotic zone. Our direct quantification of new production not only provides more precise details about key processes for NES food webs and ecosystem function, but also demonstrates the potential of this approach to be applied to other similar datasets to understand nutrient and carbon cycling in the global ocean.

eLife assessment Through anchored phylogenomic analyses, this important study offers fresh insights into the evolutionary history of the plant diet and geographic distribution of Belidae weevil beetles. Employing robust methodological approaches, the authors propose that certain belid lineages have maintained a continuous association with Araucaria hosts since the Mesozoic era. Although the biogeograph-ical analysis is somewhat limited by uncertainties in vicariance explanations, this convincing study enhances our understanding of Belidae's evolutionary dynamics and provides new perspectives on ancient community ecology. Abstract The rise of angiosperms to ecological dominance and the breakup of Gondwana during the Mesozoic marked major transitions in the evolutionary history of insect-plant interactions. To elucidate how contemporary trophic interactions were influenced by host plant shifts and palaeogeographical events, we integrated molecular data with information from the fossil record to construct a time tree for ancient phytophagous weevils of the beetle family Belidae. Our analyses indicate that crown-group Belidae originated approximately 138 Ma ago in Gondwana, associated with Pinopsida (conifer) host plants, with larvae likely developing in dead/decaying branches. Belids tracked their host plants as major plate movements occurred during Gondwana's breakup, surviving on distant, disjunct landmasses. Some belids shifted to Angiospermae and Cycadopsida when and where conifers declined, evolving new trophic interactions, including brood-pollination mutualisms with cycads and associations with achlorophyllous parasitic angiosperms. Extant radiations of belids in the genera Rhinotia (Australian region) and Proterhinus (Hawaiian Islands) have relatively recent origins.

The most distant galaxies detected were seen when the Universe was a scant 5% of its current age. At these times, progenitors of galaxies such as the Milky Way were about 10,000 times less massive. Using the James Webb Space Telescope (JWST) combined with magnification from gravitational lensing, these low-mass galaxies can not only be detected but also be studied in detail. Here we present JWST observations of a strongly lensed galaxy at zspec = 8.296 ± 0.001, showing massive star clusters (the Firefly Sparkle) cocooned in a diffuse arc in the Canadian Unbiased Cluster Survey (CANUCS)¹. The Firefly Sparkle exhibits traits of a young, gas-rich galaxy in its early formation stage. The mass of the galaxy is concentrated in 10 star clusters (49–57% of total mass), with individual masses ranging from 10⁵M⊙ to 10⁶M⊙. These unresolved clusters have high surface densities (>10³M⊙ pc⁻²), exceeding those of Milky Way globular clusters and young star clusters in nearby galaxies. The central cluster shows a nebular-dominated spectrum, low metallicity, high gas density and high electron temperature, hinting at a top-heavy initial mass function. These observations provide our first spectrophotometric view of a typical galaxy in its early stages, in a 600-million-year-old Universe.

Invention Education (IvE), a form of problem-based learning, presents new challenges for educational assessments in public schooling because traditional assessments were designed to evaluate learning in singular disciplines. This study explores the challenges and possibilities for assessing new knowledge and capabilities acquired through students’ engagement with multiple disciplines through IvE. Guided by constructivist and sociocultural theories, as well as an understanding of IvE principles and practices derived from the literature on IvE, we examine the phases of work within a national IvE program for high school students and educators. We then examine ways existing assessments align with the work at each stage of the IvE process. Findings from this case study underscore the need for a flexible assessment system with multiple measures (e.g., disciplinary knowledge and practices, skill inventories, etc.). The system must account for variations in learning contexts, individual and collective achievements, and varying lengths of time students engage in IvE.