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Abstract The current experiments searching for neutrinoless double- $$\beta $$ β ( $$0\nu \beta \beta $$ 0 ν β β ) decay also collect large statistics of Standard Model allowed two-neutrino double- $$\beta $$ β ( $$2\nu \beta \beta $$ 2 ν β β ) decay events. These can be used to search for Beyond Standard Model (BSM) physics via $$2\nu \beta \beta $$ 2 ν β β decay spectral distortions. 100Mo has a natural advantage due to its relatively short half-life, allowing higher $$2\nu \beta \beta $$ 2 ν β β decay statistics at equal exposures compared to the other isotopes. We demonstrate the potential of the dual read-out bolometric technique exploiting a 100Mo exposure of 1.47 kg $$\times $$ × years, acquired in the CUPID-Mo experiment at the Modane underground laboratory (France). We set limits on $$0\nu \beta \beta $$ 0 ν β β decays with the emission of one or more Majorons, on $$2\nu \beta \beta $$ 2 ν β β decay with Lorentz violation, and $$2\nu \beta \beta $$ 2 ν β β decay with a sterile neutrino emission. In this analysis, we investigate the systematic uncertainty induced by modeling the $$2\nu \beta \beta $$ 2 ν β β decay spectral shape parameterized through an improved model, an effect never considered before. This work motivates searches for BSM processes in the upcoming CUPID experiment, which will collect the largest amount of $$2\nu \beta \beta $$ 2 ν β β decay events among the next-generation experiments.

Abstract To understand the roles of acute-phase viral dynamics and host immune responses in post-acute sequelae of SARS-CoV-2 infection (PASC), we enrolled 136 participants within 5 days of their first positive SARS-CoV-2 real-time PCR test. Participants self-collected up to 21 nasal specimens within the first 28 days post-symptom onset; interviewer-administered questionnaires and blood samples were collected at enrollment, days 9, 14, 21, 28, and month 4 and 8 post-symptom onset. Defining PASC as the presence of any COVID-associated symptom at their 4-month visit, we compared viral markers (quantity and duration of nasal viral RNA load, infectious viral load, and plasma N-antigen level) and host immune markers (IL-6, IL-10, TNF-α, IFN-α, IFN-γ, MCP, IP-10, and Spike IgG) over the acute period. Compared to those who fully recovered, those reporting PASC demonstrated significantly higher maximum levels of SARS-CoV-2 RNA and N-antigen, burden of RNA and infectious viral shedding, and lower Spike-specific IgG levels within 9 days post-illness onset. No significant differences were identified among a panel of host immune markers. Our results suggest early viral dynamics and the associated host immune responses play a role in the pathogenesis of PASC, highlighting the importance of understanding early biological markers in the natural history of PASC.

Abstract The next generation of searches for neutrinoless double beta decay ( $$0 \nu \beta \beta $$ 0 ν β β ) are poised to answer deep questions on the nature of neutrinos and the source of the Universe’s matter–antimatter asymmetry. They will be looking for event rates of less than one event per ton of instrumented isotope per year. To claim discovery, accurate and efficient simulations of detector events that mimic $$0 \nu \beta \beta $$ 0 ν β β is critical. Traditional Monte Carlo (MC) simulations can be supplemented by machine-learning-based generative models. This work describes the performance of generative models that we designed for monolithic liquid scintillator detectors like KamLAND to produce accurate simulation data without a predefined physics model. We present their current ability to recover low-level features and perform interpolation. In the future, the results of these generative models can be used to improve event classification and background rejection by providing high-quality abundant generated data.

Does a student's high school grade point average (HSGPA) of 2.5 or greater determine or predict a successful post-secondary enrollment? For this research, historical data from the 2015-2021 cohorts of Upward Bound Catonsville (UBC) scholars were retrieved from the UBC Community College of Baltimore County (CCBC) Blumen Database report. To ensure our scholars maintain a 2.5 or better HSGPA, UBC uses several methodologies to monitor and intervene if necessary to ensure that the benchmark is being upheld including monthly check-ins with assigned program coordinators, Tutor.com, and virtual tutoring with faculty. An all hands-on-deck approach is put into play if a student slips below a 2.5 HSGPA; they are then assigned to meet with the program director until their HSGPA exceeds 2.5 HSGPA. These approaches are a combined methodology that somewhat mimics the graded response model (GRM) and hybrid data analytics (HDA). The qualitative and quantitative data show that HSGPA matters for UBC scholars with successful enrollment into post-secondary education. It is not the only thing universities or colleges use; however, it levels the playing field. The research shows that a 2.5 or higher HSGPA is recommended to enroll in post-secondary education and speaks to many characteristics of an individual's work ethic. When comparing CCBC average HSGPA of all freshmen students and all transfer students who enter the fall semesters, the UBC graduates' HSGPA is around 0.50 basis points higher. Post-secondary attainment helps lower unemployment ratios, improve quality of life, and reduce systemic poverty. If we want to solve some of America's labor ills, the findings support and suggest starting with getting a student's HSGPA to 2.5 or greater.

Many patients with cancer experience cancer-related cognitive decline (CRCD). Previous studies have shown that elevated S100β, a calcium-binding protein commonly found in glial cells, can exhibit neurotoxic effects, including disruption of the blood-brain barrier (BBB). We studied changes in S100β levels in patients with breast cancer receiving chemotherapy, and the relationship to changes in cognitive function. A total of 505 women with breast cancer (mean (sd) age; 53.4 (53.6)) and 336 age-matched controls without cancer (52.8 (10.3)) were included from a nationwide study as part of the National Cancer Institute Community Oncology Research Program (NCORP). Both groups provided blood samples and completed neurocognitive assessments within 7 days before the patients with breast cancer received their first chemotherapy dose (pre-chemotherapy; T1) and within 1 month of their last chemotherapy administration (post-chemotherapy; T2). Utilizing a linear mixed model, multivariate linear regressions, and Spearman rank correlations (rs), we investigated longitudinal changes in serum S100β concentrations and their relationships to changes in neurocognitive outcomes over time. We observed an increase in S100β for patients with breast cancer (p = 0.002), but not for controls without cancer over time (p = 0.683). Additionally, we identified subtle relationships between increases in serum S100β and worsening in cognitive performance on the Backward Counting test (rs = 0.11, p = 0.041) and self-reported FACT-Cog Perceived Cognitive Abilities (rs = −0.10, p = 0.025). Regression analyses adjusted for age, race, body-mass index (BMI), education, menopausal status, anxiety, and depression revealed a trend remained for the relationship of S100β with Backward Counting. In conclusion, we found that patients with breast cancer experience a significant increase in concentration of serum S100β over the course of chemotherapy. This increase is correlated with worsening in some neurocognitive outcomes from pre-to post-chemotherapy, with trending results remaining following adjustment for covariates.

BACKGROUND:. Early diagnostic uncertainty for infection causes delays in antibiotic administration in infected patients and unnecessary antibiotic administration in noninfected patients. OBJECTIVE:. To develop a machine learning model for the early detection of untreated infection (eDENTIFI), with the presence of infection determined by clinician chart review. DERIVATION COHORT:. Three thousand three hundred fifty-seven adult patients hospitalized between 2006 and 2018 at two health systems in Illinois, United States. VALIDATION COHORT:. We validated in 1632 patients in a third Illinois health system using area under the receiver operating characteristic curve (AUC). PREDICTION MODEL:. Using a longitudinal discrete-time format, we trained a gradient boosted machine model to predict untreated infection in the next 6 hours using routinely available patient demographics, vital signs, and laboratory results. RESULTS:. eDENTIFI had an AUC of 0.80 (95% CI, 0.79–0.81) in the validation cohort and outperformed the systemic inflammatory response syndrome criteria with an AUC of 0.64 (95% CI, 0.64–0.65; p < 0.001). The most important features were body mass index, age, temperature, and heart rate. Using a threshold with a 47.6% sensitivity, eDENTIFI detected infection a median 2.0 hours (interquartile range, 0.9–5.2 hr) before antimicrobial administration, with a negative predictive value of 93.6%. Antibiotic administration guided by eDENTIFI could have decreased unnecessary IV antibiotic administration in noninfected patients by 10.8% absolute or 46.4% relative percentage points compared with clinicians. CONCLUSION:. eDENTIFI could both decrease the time to antimicrobial administration in infected patients and unnecessary antibiotic administration in noninfected patients. Further prospective validation is needed.

IMPORTANCE:. Timely intervention for clinically deteriorating ward patients requires that care teams accurately diagnose and treat their underlying medical conditions. However, the most common diagnoses leading to deterioration and the relevant therapies provided are poorly characterized. OBJECTIVES:. We aimed to determine the diagnoses responsible for clinical deterioration, the relevant diagnostic tests ordered, and the treatments administered among high-risk ward patients using manual chart review. DESIGN, SETTING, AND PARTICIPANTS:. This was a multicenter retrospective observational study in inpatient medical-surgical wards at four health systems from 2006 to 2020. Randomly selected patients (1000 from each health system) with clinical deterioration, defined by reaching the 95th percentile of a validated early warning score, electronic Cardiac Arrest Risk Triage, were included. MAIN OUTCOMES AND MEASURES:. Clinical deterioration was confirmed by a trained reviewer or marked as a false alarm if no deterioration occurred for each patient. For true deterioration events, the condition causing deterioration, relevant diagnostic tests ordered, and treatments provided were collected. RESULTS:. Of the 4000 included patients, 2484 (62%) had clinical deterioration confirmed by chart review. Sepsis was the most common cause of deterioration (41%; n = 1021), followed by arrhythmia (19%; n = 473), while liver failure had the highest in-hospital mortality (41%). The most common diagnostic tests ordered were complete blood counts (47% of events), followed by chest radiographs (42%) and cultures (40%), while the most common medication orders were antimicrobials (46%), followed by fluid boluses (34%) and antiarrhythmics (19%). CONCLUSIONS AND RELEVANCE:. We found that sepsis was the most common cause of deterioration, while liver failure had the highest mortality. Complete blood counts and chest radiographs were the most common diagnostic tests ordered, and antimicrobials and fluid boluses were the most common medication interventions. These results provide important insights for clinical decision-making at the bedside, training of rapid response teams, and the development of institutional treatment pathways for clinical deterioration.

Abstract For the past 15 years, the proportion of honey bee hives that fail to survive winter has averaged ~ 30% in the United States. Winter hive loss has significant negative impacts on agriculture, the economy, and ecosystems. Compared to other factors, the role of honey bee gut microbial communities in driving winter hive loss has received little attention. We investigate the relationship between winter survival and honey bee gut microbiome composition of 168 honey bees from 23 hives, nine of which failed to survive through winter 2022. We found that there was a substantial difference in the abundance and community composition of honey bee gut microbiomes based on hive condition, i.e., winter survival or failure. The overall microbial abundance, as assessed using Quantitative Microbiome Profiling (QMP), was significantly greater in hives that survived winter 2022 than in those that failed, and the average overall abundance of each of ten bacterial genera was also greater in surviving hives. There were no significant differences in alpha diversity based on hive condition, but there was a highly significant difference in beta diversity. The bacterial genera Commensalibacter and Snodgrassella were positively associated with winter hive survival. Logistic regression and random forest machine learning models on pooled ASV counts for the genus data were highly predictive of winter outcome, although model performance decreased when samples from the location with no hive failures were excluded from analysis. As a whole, our results show that the abundance and community composition of honey bee gut microbiota is associated with winter hive loss, and can potentially be used as a diagnostic tool in evaluating hive health prior to the onset of winter. Future work on the functional characterization of the honey bee gut microbiome’s role in winter survival is warranted.

Abstract Marine heatwaves are increasing in frequency and duration, threatening tropical reef ecosystems through intensified coral bleaching events. We examined a strikingly variable spatial pattern of bleaching in Moorea, French Polynesia following a heatwave that lasted from November 2018 to July 2019. In July 2019, four months after the onset of bleaching, we surveyed > 5000 individual colonies of the two dominant coral genera, Pocillopora and Acropora, at 10 m and 17 m water depths, at six forereef sites around the island where temperature was measured. We found severe bleaching increased with colony size for both coral genera, but Acropora bleached more severely than Pocillopora overall. Acropora bleached more at 10 m than 17 m, likely due to higher light availability at 10 m compared to 17 m, or greater daily temperature fluctuation at depth. Bleaching in Pocillopora corals did not differ with depth but instead varied with the interaction of colony size and Accumulated Heat Stress (AHS), in that larger colonies (> 30 cm) were more sensitive to AHS than mid-size (10–29 cm) or small colonies (5–9 cm). Our findings provide insight into complex interactions among coral taxa, colony size, and water depth that produce high spatial variation in bleaching and related coral mortality.

Introduction: This research aims to characterize disparities in mpox- and vaccine-related knowledge in gay, bisexual, and other men who have sex with men in the U.S. Methods: The authors conducted a study using the American Men's Internet Survey, which includes 823 cisgender (defined as their gender identity matching their sex assigned at birth) males aged ≥15 years from August 5 to 15, 2022. The authors evaluated sociodemographic and behavioral factors associated with mpox knowledge, including race/ethnicity, region, age group, and HIV pre-exposure prophylaxis use using chi-square tests. Results: The authors identified knowledge gaps, with many participants unsure about whether individuals need 2 doses of the vaccine (34.4%) and whether the vaccine confers immediate protection (27.2%). The authors observed racial and regional disparities (p

Aim: Brain metastases (BM) are a common site of disease progression and treatment failure in non-small-cell lung cancer (NSCLC) and can be identified in up to 30–50% of patients. Although they are common, there is no standardized screening protocol for development of BM in NSCLC. Multiple clinical variables predict increased BM occurrence, and, when present, should be used to initiate screening MRI.Materials & methods: We performed a single center retrospective review of NSCLC patients, examining BM development and overall survival. Available clinical, radiographic and molecular data were reviewed for association with BM and overall survival. A predictive model for BM development was created for multivariate analysis.Results: Risk factors for new BM development in NSCLC included younger age, larger primary lung tumor, Karnofsky performance score (KPS)