A deep search for large complex organic species toward IRAS16293-2422 B at 3 mm with ALMA

Complex organic molecules (COMs) have been detected ubiquitously in protostellar systems. However, at shorter wavelengths ($sim 0.8$\,mm), it is generally more difficult to detect larger molecules than at longer wavelengths sim 3$\,mm) because of the increase in millimeter dust opacity, line confusion, and unfavorable partition function. We aim to search for large molecules (more than eight atoms) in the Atacama Large Millimeter/submillimeter Array (ALMA) Band 3 spectrum of IRAS 16293-2422 B. In particular, the goal is to quantify the usability of ALMA Band 3 for molecular line surveys in comparison to similar studies at shorter wavelengths. We used deep ALMA Band 3 observations of IRAS 16293-2422 B to search for more than 70 molecules and identified as many lines as possible in the spectrum. The spectral settings were set to specifically target three-carbon species such as i- and n-propanol and glycerol, the next step after glycolaldehyde and ethylene glycol in the hydrogenation of CO. We then derived the column densities and excitation temperatures of the detected species and compared the ratios with respect to methanol between Band 3 ($ sim 3$\,mm) and Band 7 sim 1$\,mm, Protostellar Interferometric Line Survey) observations of this source to examine the effect of the dust optical depth. We identified lines of 31 molecules including many oxygen-bearing COMs such as CH$_3$OH, CH$_2$OHCHO, CH$_3$CH$_2$OH, and c-C$_2$H$_4$O and a few nitrogen- and sulfur-bearing ones such as HOCH$_2$CN and CH$_3$SH. The largest detected molecules are gGg-(CH$_2$OH)$_2$ and CH$_3$COCH$_3$. We did not detect glycerol or i- and n-propanol, but we do provide upper limits for them which are in line with previous laboratory and observational studies. The line density in Band 3 is only $sim 2.5$ times lower in frequency space than in Band 7. From the detected lines in Band 3 at a $level sim 25-30$ of them could not be identified indicating the need for more laboratory data of rotational spectra. We find similar column densities and column density ratios of COMs (within a factor $sim 2$) between Band 3 and Band 7. The effect of the dust optical depth for IRAS 16293-2422 B at an off-source location on column densities and column density ratios is minimal. Moreover, for warm protostars, long wavelength spectra ($sim 3$\,mm) are not only crowded and complex, but they also take significantly longer integration times than shorter wavelength observations sim 0.8$\,mm) to reach the same sensitivity limit. The 3\,mm search has not yet resulted in the detection of larger and more complex molecules in warm sources. A full deep ALMA Band$2-3$(i.e. sim 3-4$\,mm wavelengths) survey is needed to assess whether low frequency data have the potential to reveal more complex molecules in warm sources

Information as a regulatory instrument to price biodiversity benefits: Certification and ecolabeling policy practices

10.1007/s10531-004-2529-3Biodiversity and Conservation1482009-2027BONS

The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection fatality rate (IFR) doubles with every 5 y of age from childhood onward. Circulating autoantibodies neutralizing IFN-α, IFN-ω, and/or IFN-β are found in ∼20% of deceased patients across age groups, and in ∼1% of individuals aged <70 y and in >4% of those >70 y old in the general population. With a sample of 1,261 unvaccinated deceased patients and 34,159 individuals of the general population sampled before the pandemic, we estimated both IFR and relative risk of death (RRD) across age groups for individuals carrying autoantibodies neutralizing type I IFNs, relative to noncarriers. The RRD associated with any combination of autoantibodies was higher in subjects under 70 y old. For autoantibodies neutralizing IFN-α2 or IFN-ω, the RRDs were 17.0 (95% CI: 11.7 to 24.7) and 5.8 (4.5 to 7.4) for individuals <70 y and ≥70 y old, respectively, whereas, for autoantibodies neutralizing both molecules, the RRDs were 188.3 (44.8 to 774.4) and 7.2 (5.0 to 10.3), respectively. In contrast, IFRs increased with age, ranging from 0.17% (0.12 to 0.31) for individuals <40 y old to 26.7% (20.3 to 35.2) for those ≥80 y old for autoantibodies neutralizing IFN-α2 or IFN-ω, and from 0.84% (0.31 to 8.28) to 40.5% (27.82 to 61.20) for autoantibodies neutralizing both. Autoantibodies against type I IFNs increase IFRs, and are associated with high RRDs, especially when neutralizing both IFN-α2 and IFN-ω. Remarkably, IFRs increase with age, whereas RRDs decrease with age. Autoimmunity to type I IFNs is a strong and common predictor of COVID-19 death

Autoantibodies neutralizing type I IFNs are present in ~4% of uninfected individuals over 70 years old and account for ~20% of COVID-19 deaths

Circulating autoantibodies (auto-Abs) neutralizing high concentrations (10 ng/ml; in plasma diluted 1:10) of IFN-α and/or IFN-ω are found in about 10% of patients with critical COVID-19 (coronavirus disease 2019) pneumonia but not in individuals with asymptomatic infections. We detect auto-Abs neutralizing 100-fold lower, more physiological, concentrations of IFN-α and/or IFN-ω (100 pg/ml; in 1:10 dilutions of plasma) in 13.6% of 3595 patients with critical COVID-19, including 21% of 374 patients >80 years, and 6.5% of 522 patients with severe COVID-19. These antibodies are also detected in 18% of the 1124 deceased patients (aged 20 days to 99 years; mean: 70 years). Moreover, another 1.3% of patients with critical COVID-19 and 0.9% of the deceased patients have auto-Abs neutralizing high concentrations of IFN-β. We also show, in a sample of 34,159 uninfected individuals from the general population, that auto-Abs neutralizing high concentrations of IFN-α and/or IFN-ω are present in 0.18% of individuals between 18 and 69 years, 1.1% between 70 and 79 years, and 3.4% >80 years. Moreover, the proportion of individuals carrying auto-Abs neutralizing lower concentrations is greater in a subsample of 10,778 uninfected individuals: 1% of individuals <70 years, 2.3% between 70 and 80 years, and 6.3% >80 years. By contrast, auto-Abs neutralizing IFN-β do not become more frequent with age. Auto-Abs neutralizing type I IFNs predate SARS-CoV-2 infection and sharply increase in prevalence after the age of 70 years. They account for about 20% of both critical COVID-19 cases in the over 80s and total fatal COVID-19 cases. © 2021 The Authors, some rights reserved

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024