Background: Carbapenem-resistant Enterobacterales (CRE) have become an increasing public health challenge in the United States over the past two decades. Carbapenemase-producing CREs (CP-CREs) significantly contribute to the spread of antimicrobial-resistant pathogens in healthcare settings. Tennessee has been conducting surveillance of CRE since 2011. As part of the Emerging Infections Program (EIP), the state has participated in population-based surveillance in Davidson and seven surrounding counties, collaborating with the Centers for Disease Control and Prevention (CDC) since 2014. Methods: The data collected through the Muti-site Gram-negative Surveillance Initiative (MuGSI) project, a collaboration between Tennessee and CDC as part of EIP, was used for this study. The analysis was performed on a subset of CRE isolates tested for carbapenemase production (CP) among all incident CRE cases collected from 2016 to 2022. Incident CRE cases are defined as the identification of carbapenem-resistant E. coli, Enterobacter cloacae complex, and Klebsiella species (K. aerogenes, K. oxytoca, K. pneumoniae, and K. variicola) from urine or normally sterile specimens (e.g., blood) from the residents of the surveillance area in a 30-day period. The mortality data was obtained from the Tennessee Vital Registry and merged with the surveillance data. Cox regression analysis was performed to evaluate if there is a difference in the 90-day survival rate based on the CP status of the pathogen, gender, age group, and the Charlson comorbidity index (CCI) score. Data analysis was done using SAS version 9.4. Results: There were 570 CRE cases reported during the study period (2016-2022). Of these, 406 were tested for carbapenemase production and 87 (21.4%) were positive for CP. There were 269 (66.3%) females and 137 (33.7%) males. Patients with higher Charlson comorbidity index score (> = 5) have significantly higher hazard ratios compared to those with low scores (HR 4.17; p-value) Conclusion: This study indicates that patients infected with CP-CRE, females, and those with high Charlson comorbidity index score have a significantly higher probability of dying within 90 days. These factors are worth considering when conducting a risk assessment of patients infected with drug-resistant gram-negative bacilli. The significantly increased risk of death among patients infected with CP-CRE highlights the need for timely carbapenemase testing and use of the test result for appropriate antimicrobial therapy and infection prevention.

The stars of the Milky Way carry the chemical history of our Galaxy in their atmospheres as they journey through its vast expanse. Like barcodes, we can extract the chemical fingerprints of stars from high-resolution spectroscopy. The fourth data release (DR4) of the Galactic Archaeology with HERMES (GALAH) Survey, based on a decade of observations, provides the chemical abundances of up to 32 elements for 917 588 stars that also have exquisite astrometric data from the Gaia satellite. For the first time, these elements include life-essential nitrogen to complement carbon, and oxygen as well as more measurements of rare-earth elements critical to modern-life electronics, offering unparalleled insights into the chemical composition of the Milky Way. For this release, we use neural networks to simultaneously fit stellar parameters and abundances across the whole wavelength range, leveraging synthetic grids computed with Spectroscopy Made Easy. These grids account for atomic line formation in non-local thermodynamic equilibrium for 14 elements. In a two-iteration process, we first fit stellar labels to all 1 085 520 spectra, then co-add repeated observations and refine these labels using astrometric data from Gaia and 2MASS photometry, improving the accuracy and precision of stellar parameters and abundances. Our validation thoroughly assesses the reliability of spectroscopic measurements and highlights key caveats. GALAH DR4 represents yet another milestone in Galactic archaeology, combining detailed chemical compositions from multiple nucleosynthetic channels with kinematic information and age estimates. The resulting dataset, covering nearly a million stars, opens new avenues for understanding not only the chemical and dynamical history of the Milky Way but also the broader questions of the origin of elements and the evolution of planets, stars, and galaxies.

Early gut microbiome development may impact brain and behavioral development. Using a nonhuman primate model (Macaca mulatta), we investigated the association between social environments and the gut microbiome on infant neurodevelopment and cognitive function. Infant rhesus monkeys (n = 33) were either mother-peer-reared (MPR) or nursery-reared (NR). Neurodevelopmental outcomes, namely emotional responsivity, visual orientation, and motor maturity, were assessed with the Primate Neonatal Neurobehavioral Assessment (PNNA) at 14–30 days. Cognitive development was assessed through tasks evaluating infant reward association, cognitive flexibility, and impulsivity at 6–8 months. The fecal microbiome was quantified from rectal swabs via 16S rRNA sequencing. Factor analysis was used to identify “co-abundance factors” describing patterns of microbial composition. We used multiple linear regressions with AIC Model Selection and differential abundance analysis (MaAsLin2) to evaluate relationships between co-abundance factors, microbiome diversity, and neuro-/cognitive development outcomes. At 30 days of age, a gut microbiome co-abundance factor, or pattern, with high Prevotella and Lactobacillus (β = −0.88, p = 0.04, AIC Weight = 68%) and gut microbiome alpha diversity as measured by Shannon diversity (β = −1.33, p = 0.02, AIC Weight = 80%) were both negatively associated with infant emotional responsivity. At 30 days of age, being NR was also associated with lower emotional responsivity (Factor 1 model: β = −3.13, p < 0.01; Shannon diversity model: β = −3.77, p < 0.01). The infant gut microbiome, along with early-rearing environments, may shape domains of neuro-/cognitive development related to temperament.

Conventional survey tools such as weighting do not address non-ignorable nonresponse that occurs when nonresponse depends on the variable being measured. This paper describes non-ignorable nonresponse weighting and imputation models using randomized response instruments, which are variables that affect response but not the outcome of interest. This paper uses a doubly robust estimator that is valid if one, but not necessarily both, of the weighting and imputation models is correct. When applied to a national 2019 survey, these tools produce estimates that suggest there was nontrivial non-ignorable nonresponse related to turnout, and, for subgroups, Trump approval and policy questions. For example, the conventional MAR-based weighted estimates of Trump support in the Midwest were 10 percentage points lower than the MNAR-based estimates.