The rising star hypothesis proposes that characteristics of “rising star” protégés already set them up for success, such that mentoring is a byproduct of their ambition. Alternatively, the influential mentor hypothesis states that protégés experience success due to the positive influence of mentoring. Herein, we test whether rising star characteristics (need for achievement and career initiative behaviors) precede or follow informal mentorship. Using data from a sample of 94 protégés (recruited from a hospital) over two time points (six-week lag), we found best evidence for a mix of the rising star and influential mentor hypotheses, in which career initiative behaviors predict the number of informal mentors, and in turn, need for achievement. Cross-lagged effects were probed and provide evidence that the benefits of informal mentoring does not seem to extend to formal mentoring. Practical implications are highlighted, including the benefits of multiple mentors and alternatives to traditional mentoring. © The Author(s) 2025.

In this study, we investigate the role of matching communication (i.e., relational messages received) in mentoring outcomes (mentor and protégé career attitudes). Specifically, we used data from a sample of 145 matched mentor–protégé dyads in a response surface analysis to show that matched relational messaging generally relates to mentors (and less consistently, protégés) reporting enhanced career satisfaction and career commitment. Furthermore, our findings are consistent with previous research showing that when relational messages (i.e., intimacy) or self-disclosure are matched at high or low (i.e., more extreme) levels, the mentor and protégé have the best outcomes. Additionally, beneficial mentor outcomes were maximized when levels of seriousness were matched at a moderate level. These results suggest that both levels of relational messaging, as well as the degree to which mentors and protégé match on these constructs influences mentoring outcomes. Study limitations, future directions for research, and implications for career development are discussed. © The Author(s) 2024.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naïve baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.Competing Interest StatementAV, MC, and APP report grants from Metabiota Inc outside the submitted work.Funding StatementFor teams that reported receiving funding for their work, we report the sources and disclosures below. CMU-TimeSeries: CDC Center of Excellence, gifts from Google and Facebook. CU-select: NSF DMS-2027369 and a gift from the Morris-Singer Foundation. COVIDhub: This work has been supported by the US Centers for Disease Control and Prevention (1U01IP001122) and the National Institutes of General Medical Sciences (R35GM119582). The content is solely the responsibility of the authors and does not necessarily represent the official views of CDC, NIGMS or the National Institutes of Health. Johannes Bracher was supported by the Helmholtz Foundation via the SIMCARD Information& Data Science Pilot Project. Tilmann Gneiting gratefully acknowledges support by the Klaus Tschira Foundation. DDS-NBDS: NSF III-1812699. EPIFORECASTS-ENSEMBLE1: Wellcome Trust (210758/Z/18/Z) GT_CHHS-COVID19: William W. George Endowment, Virginia C. and Joseph C. Mello Endowments, NSF DGE-1650044, NSF MRI 1828187, research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech, and the following benefactors at Georgia Tech: Andrea Laliberte, Joseph C. Mello, Richard Rick E. & Charlene Zalesky, and Claudia & Paul Raines GT-DeepCOVID: CDC MInD-Healthcare U01CK000531-Supplement. NSF (Expeditions CCF-1918770, CAREER IIS-2028586, RAPID IIS-2027862, Medium IIS-1955883, NRT DGE-1545362), CDC MInD program, ORNL and funds/computing resources from Georgia Tech and GTRI. IHME: This work was supported by the Bill & Melinda Gates Foundation, as well as funding from the state of Washington and the National Science Foundation (award no. FAIN: 2031096). IowaStateLW-STEM: Iowa State University Plant Sciences Institute Scholars Program, NSF DMS-1916204, NSF CCF-1934884, Laurence H. Baker Center for Bioinformatics and Biological Statistics. JHU_IDD-CovidSP: State of California, US Dept of Health and Human Services, US Dept of Homeland Security, US Office of Foreign Disaster Assistance, Johns Hopkins Health System, Office of the Dean at Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University Modeling and Policy Hub, Centers fo Disease Control and Prevention (5U01CK000538-03), University of Utah Immunology, Inflammation, & Infectious Disease Initiative (26798 Seed Grant). LANL-GrowthRate: LANL LDRD 20200700ER. MOBS-GLEAM_COVID: COVID Supplement CDC-HHS-6U01IP001137-01. NotreDame-mobility and NotreDame-FRED: NSF RAPID DEB 2027718 UA-EpiCovDA: NSF RAPID Grant # 2028401. UCSB-ACTS: NSF RAPID IIS 2029626. UCSD-NEU: Google Faculty Award, DARPA W31P4Q-21-C-0014, COVID Supplement CDC-HHS-6U01IP001137-01. UMass-MechBayes: NIGMS R35GM119582, NSF 1749854. UMich-RidgeTfReg: The University of Michigan Physics Department and the University of Michigan Office of Research.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:UMass-Amherst IRBAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data and code referred to in the manuscript are publicly available. https://github.com/reichlab/covid19-forecast-hub/ https://github.com/reichlab/covidEnsembles https://zoltardata.com/project/44

BACKGROUND: COVID-19 vaccines may be co-administered with other recommended vaccines, including seasonal influenza vaccines. However, few studies have evaluated the safety of co-administration of mRNA COVID-19 and seasonal influenza vaccines. OBJECTIVE: To describe reports to the Vaccine Adverse Event Reporting System (VAERS) after co-administration of mRNA COVID-19 and seasonal influenza vaccines. METHODS: We searched the VAERS database for reports of adverse events (AEs) following co-administration of mRNA COVID-19 and seasonal influenza vaccines and following a first booster dose mRNA COVID-19 vaccine alone, during July 1, 2021-June 30, 2022. We assessed the characteristics of these reports and described the most frequently reported MedDRA preferred terms (PTs). Clinicians reviewed available medical records for serious reports and reports of adverse events of special interest (AESI) and categorized the main diagnosis by system organ class. RESULTS: From July 1, 2021 through June 30, 2022, VAERS received 2,449 reports of adverse events following co-administration of mRNA COVID-19 and seasonal influenza vaccines. Median age of vaccinees was 48 years (IQR: 31, 66); 387 (15.8%) were classified as serious. Most reports (1,713; 69.3%) described co-administration of a first booster dose of an mRNA COVID-19 vaccine with seasonal influenza vaccine. The most common AEs among non-serious reports were injection site reactions (193; 14.5%), headache (181; 13.6%), and pain (171; 12.8%). The most common AEs among reports classified as serious were dyspnea (38; 14.9%), COVID-19 infection (32; 12.6%), and chest pain (27; 10.6%). DISCUSSION: This review of reports to VAERS following co-administration of mRNA COVID-19 and seasonal influenza vaccines did not reveal any unusual or unexpected patterns of AEs. Increased reporting of certain events (e.g., COVID-19 disease) was expected. CDC will continue to monitor the safety of co-administration of mRNA COVID-19 and seasonal influenza vaccines, including co-administration involving bivalent mRNA COVID-19 booster vaccines that have been recommended for people ages ≥ 6 months in the United States.

SignificanceThis paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.

We measured contact patterns using online diaries for 304 employees of 3 U.S. companies working remotely. The median number of daily contacts was 2 (IQR 1-4); majority were conversation (55 %), occurred at home (64 %) and lasted >4 h (38 %). These data are crucial for modeling outbreak control among the workforces.

BACKGROUND: Forests in urban landscapes differ from their rural counterparts in ways that may alter vector-borne disease dynamics. In urban forest fragments, tick-borne pathogen prevalence is not well characterized; mitigating disease risk in densely-populated urban landscapes requires understanding ecological factors that affect pathogen prevalence. We trapped blacklegged tick (Ixodes scapularis) nymphs in urban forest fragments on the East Coast of the United States and used multiplex real-time PCR assays to quantify the prevalence of four zoonotic, tick-borne pathogens. We used Bayesian logistic regression and WAIC model selection to understand how vegetation, habitat, and landscape features of urban forests relate to the prevalence of B. burgdorferi (the causative agent of Lyme disease) among blacklegged ticks. RESULTS: In the 258 nymphs tested, we detected Borrelia burgdorferi (11.2% of ticks), Borrelia miyamotoi (0.8%) and Anaplasma phagocytophilum (1.9%), but we did not find Babesia microti (0%). Ticks collected from forests invaded by non-native multiflora rose (Rosa multiflora) had greater B. burgdorferi infection rates (mean = 15.9%) than ticks collected from uninvaded forests (mean = 7.9%). Overall, B. burgdorferi prevalence among ticks was positively related to habitat features (e.g. coarse woody debris and total understory cover) favorable for competent reservoir host species. CONCLUSIONS: Understory structure provided by non-native, invasive shrubs appears to aggregate ticks and reservoir hosts, increasing opportunities for pathogen transmission. However, when we consider pathogen prevalence among nymphs in context with relative abundance of questing nymphs, invasive plants do not necessarily increase disease risk. Although pathogen prevalence is greater among ticks in invaded forests, the probability of encountering an infected tick remains greater in uninvaded forests characterized by thick litter layers, sparse understories, and relatively greater questing tick abundance in urban landscapes.

Wood pellet storage safety is an important aspect for implementing woody biomass as a renewable energy source. When wood pellets are stored indoors in large quantities (tons) in poorly ventilated spaces in buildings, such as in basements, off-gassing of volatile organic compounds (VOCs) can significantly affect indoor air quality. To determine the emission rates and potential impact of VOC emissions, a series of laboratory and field measurements were conducted using softwood, hardwood, and blended wood pellets manufactured in New York. Evacuated canisters were used to collect air samples from the headspace of drums containing pellets and then in basements and pellet storage areas of homes and small businesses. Multiple peaks were identified during GC/MS and GC/FID analysis, and four primary VOCs were characterized and quantified: methanol, pentane, pentanal, and hexanal. Laboratory results show that total VOCs (TVOCs) concentrations for softwood (SW) were statistically (p < 0.02) higher than blended or hardwood (HW) (SW: 412 ± 25; blended: 203 ± 4; HW: 99 ± 8, ppb). The emission rate from HW was the fastest, followed by blended and SW, respectively. Emissions rates were found to range from 10-1 to 10-5 units, depending upon environmental factors. Field measurements resulted in airborne concentrations ranging from 67 ± 8 to 5000 ± 3000 ppb of TVOCs and 12 to 1500 ppb of aldehydes, with higher concentrations found in a basement with a large fabric bag storage unit after fresh pellet delivery and lower concentrations for aged pellets. These results suggest that large fabric bag storage units resulted in a substantial release of VOCs into the building air. Occupants of the buildings tested discussed concerns about odor and sensory irritation when new pellets were delivered. The sensory response was likely due to the aldehydes. © 2015 American Chemical Society.

CASE DESCRIPTION: In April 2012, Salmonella enterica serotype Infantis was detected in an unopened bag of dry dog food collected during routine retail surveillance. PulseNet, a national bacterial subtyping network, identified humans with Salmonella Infantis infection with the same genetic fingerprint as the dog food sample. CLINICAL FINDINGS: An outbreak investigation identified 53 ill humans infected with the outbreak strain during January 1 to July 5, 2012, in 21 states and 2 provinces in Canada; 20 (38%) were children ≤ 2 years old, and 12 of 37 (32%) were hospitalized. Of 21 ill people who remembered the dog food brand, 12 (57%) reported a brand produced at a plant in Gaston, SC. Traceback investigations also identified that plant. The outbreak strain was isolated from bags of dry dog food and fecal specimens obtained from dogs that lived with ill people and that ate the implicated dry dog food. TREATMENT AND OUTCOME: The plant was closed temporarily for cleaning and disinfection. Sixteen brands involving > 27,000 metric tons (> 30,000 tons) of dry dog and cat food were recalled. Thirty-one ill dogs linked to recalled products were reported through the FDA consumer complaint system. CLINICAL RELEVANCE: A one-health collaborative effort on epidemiological, laboratory, and traceback investigations linked dry dog foods produced at a plant to illnesses in dogs and humans. More efforts are needed to increase awareness among pet owners, health-care professionals, and the pet food industry on the risk of illness in pets and their owners associated with dry pet foods and treats.