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Lithium-ion batteries (LIBs) represent the state of the art in high-density energy storage. To further advance LIB technology, a fundamental understanding of the underlying chemical processes is required. In particular, the decomposition of electrolyte species and associated formation of the solid electrolyte interphase (SEI) is critical for LIB performance. However, SEI formation is poorly understood, in part due to insufficient exploration of the vast reactive space. The Lithium-Ion Battery Electrolyte (LIBE) dataset reported here aims to provide accurate first-principles data to improve the understanding of SEI species and associated reactions. The dataset was generated by fragmenting a set of principal molecules, including solvents, salts, and SEI products, and then selectively recombining a subset of the fragments. All candidate molecules were analyzed at the ω B97X-V/def2-TZVPPD/SMD level of theory at various charges and spin multiplicities. In total, LIBE contains structural, thermodynamic, and vibrational information on over 17,000 unique species. In addition to studies of reactivity in LIBs, this dataset may prove useful for machine learning of molecular and reaction properties. Measurement(s) molecule • solid electrolyte interphase Technology Type(s) density functional theory • computational modeling technique Factor Type(s) bond type • charge • spin multiplicity Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14915256

Foundation models, including large language models, vision-language models, and similar large-scale machine learning tools, are quickly becoming ubiquitous in society and in the professional world. Chemical practitioners are not immune to the appeal of foundation models, nor are they immune to the many risks and harms that these models introduce. In this work, I present the first analysis of foundation models using the combined lens of scientific ethics and chemical professional ethics. I find that general-purpose generative foundation models are in many ways incompatible with the moral practice of chemistry, though there are fewer ethical problems with chemistry-specific foundation models. My discussion concludes with an examination of how the harm associated with foundation models can be minimized and further poses a set of serious lingering questions for chemical practitioners and scientific ethicists.

The receptor for advanced glycation end products (RAGE) is a key contributor to immune and inflammatory responses in myriad diseases. RAGE is a transmembrane pattern recognition receptor with a special interest in pulmonary anomalies due to its naturally abundant pulmonary expression. Our previous studies demonstrated an inflammatory role for RAGE following acute 30-day exposure to secondhand smoke (SHS), wherein immune cell diapedesis and cytokine/chemokine secretion were accentuated in part via RAGE signaling. However, the chronic inflammatory mechanisms associated with RAGE have yet to be fully elucidated. In this study, we address the impact of long-term SHS exposure on RAGE signaling. RAGE knockout (RKO) and wild-type (WT) mice were exposed to SHS using a nose-only delivery system (Scireq Scientific, Montreal, Canada) for six months. SHS-exposed animals were compared to mice exposed to room air (RA) only. Immunoblotting was used to assess the phospho-AKT and phospho-ERK activation data, and colorimetric high-throughput assays were used to measure NF-kB. Ras activation was measured via ELISAs. Bronchoalveolar lavage fluid (BALF) cellularity was quantified, and a mouse cytokine antibody array was used to screen the secreted cytokines. The phospho-AKT level was decreased, while those of phospho-ERK, NF-kB, and Ras were elevated in both groups of SHS-exposed mice, with the RKO + SHS-exposed mice demonstrating significantly decreased levels of each intermediate compared to those of the WT + SHS-exposed mice. The BALF contained increased levels of diverse pro-inflammatory cytokines in the SHS-exposed WT mice, and diminished secretion was detected in the SHS-exposed RKO mice. These results validate the role for RAGE in the mediation of chronic pulmonary inflammatory responses and suggest ERK signaling as a likely pathway that perpetuates RAGE-dependent inflammation. Additional characterization of RAGE-mediated pulmonary responses to prolonged exposure will provide a valuable insight into the cellular mechanisms of lung diseases such as chronic obstructive pulmonary disease.

Exposure to secondhand smoke (SHS) during fetal development results in negative postnatal effects, including altered organ development, changes in metabolism, and increased risk of respiratory illness. Previously, we found the induction of intrauterine growth restriction (IUGR) dependent on the expression of the receptor for advanced glycation end-products (RAGE) in mice treated with SHS. Furthermore, antenatal SHS exposure increases RAGE expression in the fetal lung. Our objective was to determine the postnatal effects of antenatal SHS treatment in 4- and 12-week-old offspring. Pregnant animals were treated with SHS via a nose-only delivery system (Scireq Scientific, Montreal, Canada) for 4 days (embryonic day 14.5 through 18.5), and offspring were evaluated at 4 or 12 weeks of age. Animal and organ weights were measured, and lungs were histologically characterized. Blood pressure and heart rates were obtained, and RAGE protein expression was determined in the lungs of control and treated animals. We observed the following: (1) significant decreases in animal, liver, and heart weights at 4 weeks of age; (2) increased blood pressure in 4-week-old animals; and (3) increased RAGE expression in the lungs of the 4-week-old animals. Our results suggest an improvement in these metrics by 12 weeks postnatally such that measures were not different regardless of RA or SHS exposure. Increased RAGE expression in lungs from 4-week-old mice antenatally treated with SHS suggests a possible role for this important smoke-mediated receptor in establishing adult disease following IUGR pregnancies.

Receptors for advanced glycation end-products (RAGE) are multi-ligand cell surface receptors of the immunoglobin superfamily prominently expressed by lung epithelium. Previous experiments demonstrated that over-expression of RAGE by murine alveolar epithelium throughout embryonic development causes neonatal lethality coincident with significant lung hypoplasia. In the current study, we evaluated the expression of NKX2.1 (also referred to as TTF-1), a homeodomain-containing transcription factor critical for branching morphogenesis, in mice that differentially expressed RAGE. We also contextualized NKX2.1 expression with the abundance of FoxA2, a winged double helix DNA binding protein that influences respiratory epithelial cell differentiation and surfactant protein expression. Conditional RAGE over-expression was induced in mouse lung throughout gestation (embryonic day E0–18.5), as well as during the critical saccular period of development (E15.5–18.5), and analyses were conducted at E18.5. Histology revealed markedly less lung parenchyma beginning in the canalicular stage of lung development and continuing throughout the saccular period. We discovered consistently decreased expression of both NKX2.1 and FoxA2 in lungs from transgenic (TG) mice compared to littermate controls. We also observed diminished surfactant protein C in TG mice, suggesting possible hindered differentiation and/or proliferation of alveolar epithelial cells under the genetic control of these two critical transcription factors. These results demonstrate that RAGE must be specifically regulated during lung formation. Perturbation of epithelial cell differentiation culminating in respiratory distress and perinatal lethality may coincide with elevated RAGE expression in the lung parenchyma.

Recent advances in robotic design, autonomy and sensor integration create solutions for the exploration of deep-sea environments, transferable to the oceans of icy moons. Marine platforms do not yet have the mission autonomy capacity of their space counterparts (e.g., the state of the art Mars Perseverance rover mission), although different levels of autonomous navigation and mapping, as well as sampling, are an extant capability. In this setting their increasingly biomimicked designs may allow access to complex environmental scenarios, with novel, highly-integrated life-detecting, oceanographic and geochemical sensor packages. Here, we lay an outlook for the upcoming advances in deep-sea robotics through synergies with space technologies within three major research areas: biomimetic structure and propulsion (including power storage and generation), artificial intelligence and cooperative networks, and life-detecting instrument design. New morphological and material designs, with miniaturized and more diffuse sensor packages, will advance robotic sensing systems. Artificial intelligence algorithms controlling navigation and communications will allow the further development of the behavioral biomimicking by cooperating networks. Solutions will have to be tested within infrastructural networks of cabled observatories, neutrino telescopes, and off-shore industry sites with agendas and modalities that are beyond the scope of our work, but could draw inspiration on the proposed examples for the operational combination of fixed and mobile platforms.

Introduction H‐index is a widely used metric quantifying a researcher's productivity and impact based on an author's publications and citations. Though convenient to calculate, h‐index fails to incorporate collaborations and interrelationships between physicians into its assessment of academic impact, leading to limited insight into grouped networks. We present social network analysis as a tool to measure relationships between physicians and quantify their academic impact. Methods A bibliometric multicenter analysis was conducted on physician faculty from 129 US ACGME accredited otolaryngology programs who have publications with a physician co‐author in the field. Using web searches, 2494 physician faculty were identified. Scopus IDs, h‐indices, and publication data for these physicians were identified using multiple Elsevier APIs queried in December 2023. Publications with multiple otolaryngology physician co‐authors were included. Network and sub network maps were generated using Gephi and analyzed with custom R scripts. Centrality measures (degree, PageRank, betweenness centralities) quantified collaboration propensity. Non‐parametric correlation analysis between centrality measures and h‐index was conducted. Sankey diagrams were plotted using ggplot2. Results A co‐authorship network of 2259 physicians was constructed. Physicians were visualized as nodes with collaborations as links. Centrality measures correlated strongly with h‐index (h‐index vs. degree centrality: r2 = 0.62, h‐index vs. PageRank: r2 = 0.55, h‐index vs. betweenness centrality: r2 = 0.55; p < .0001). Analysis revealed novel insights into physician network structure, identifying 14 communities primarily populated by single subspecialties with varied node density. Conclusion Social network analysis showed moderate correlation between social connectedness measures and h‐index, supporting its use in measuring academic impact. In otolaryngology, collaborative interactions within the academic community are strongly shaped by sub‐specialty affiliation and academic institution. We utilize social network analysis as a new methodology to investigate the relationships between faculty physicians in all US ACGME accredited otolaryngology programs. We show that network analysis provides new metrics to evaluate a physician's academic impact and reveals collaborative subgroups of physicians and institutions within otolaryngology. This method offers an additional way to look at physician impact other than h‐index and lays the foundation for future exploration of other specialties and a more nuanced understanding of academic medicine.

The development of algorithms for agile science and autonomous exploration has been pursued in contexts ranging from spacecraft to planetary rovers to unmanned aerial vehicles to autonomous underwater vehicles. In situations where time, mission resources and communications are limited and the future state of the operating environment is unknown, the capability of a vehicle to dynamically respond to changing circumstances without human guidance can substantially improve science return. Such capabilities are difficult to achieve in practice, however, because they require intelligent reasoning to utilize limited resources in an inherently uncertain environment. Here we discuss the development, characterization and field performance of two algorithms for autonomously collecting water samples on VALKYRIE (Very deep Autonomous Laser-powered Kilowatt-class Yo-yoing Robotic Ice Explorer), a glacier-penetrating cryobot deployed to the Matanuska Glacier, Alaska (Mission Control location: 61°42′09.3″N 147°37′23.2″W). We show performance on par with human performance across a wide range of mission morphologies using simulated mission data, and demonstrate the effectiveness of the algorithms at autonomously collecting samples with high relative cell concentration during field operation. The development of such algorithms will help enable autonomous science operations in environments where constant real-time human supervision is impractical, such as penetration of ice sheets on Earth and high-priority planetary science targets like Europa.

The Ralph Lauren CEO, who's been in the corner office for eight years, is taking home the WWD Edward Nardoza Honor for CEO Creative Leadership.

Multisystem inflammatory syndrome in children (MIS-C) presents with fever, inflammation and pathology of multiple organs in individuals under 21 years of age in the weeks following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Although an autoimmune pathogenesis has been proposed, the genes, pathways and cell types causal to this new disease remain unknown. Here we perform RNA sequencing of blood from patients with MIS-C and controls to find disease-associated genes clustered in a co-expression module annotated to CD56 dim CD57 + natural killer (NK) cells and exhausted CD8 + T cells. A similar transcriptome signature is replicated in an independent cohort of Kawasaki disease (KD), the related condition after which MIS-C was initially named. Probing a probabilistic causal network previously constructed from over 1,000 blood transcriptomes both validates the structure of this module and reveals nine key regulators, including TBX21, a central coordinator of exhausted CD8 + T cell differentiation. Together, this unbiased, transcriptome-wide survey implicates downregulation of NK cells and cytotoxic T cell exhaustion in the pathogenesis of MIS-C. Multisystem inflammatory syndrome in children (MIS-C) onsets in COVID-19 patients with manifestations similar to Kawasaki disease (KD). Here the author probe the peripheral blood transcriptome of MIS-C patients to find signatures related to natural killer (NK) cell activation and CD8+ T cell exhaustion that are shared with KD patients.