The Dense Gas Mass Fraction and the Relationship to Star Formation in M51

Observations of 12CO J = 1 - 0 and HCN J = 1 - 0 emission from NGC 5194 (M51) made with the 50 m Large Millimeter Telescope and the SEQUOIA focal plane array are presented. Using the HCN-to-CO ratio, we examine the dense gas mass fraction over a range of environmental conditions within the galaxy. Within the disk, the dense gas mass fraction varies along the spiral arms but the average value over all spiral arms is comparable to the mean value of interarm regions. We suggest that the near-constant dense gas mass fraction throughout the disk arises from a population of density-stratified, self-gravitating molecular clouds and the required density threshold to detect each spectral line. The measured dense gas fraction significantly increases in the central bulge in response to the effective pressure, P e , from the weight of the stellar and gas components. This pressure modifies the dynamical state of the molecular cloud population and, possibly, the HCN-emitting regions in the central bulge from self-gravitating to diffuse configurations in which P e is greater than the gravitational energy density of individual clouds. Diffuse molecular clouds comprise a significant fraction of the molecular gas mass in the central bulge, which may account for the measured sublinear relationships between the surface densities of the star formation rate and molecular and dense gas

An Outcome Assessment of a Single Institution\u27s Longitudinal Experience with Uveal Melanoma Patients with Liver Metastasis.

There is no FDA-approved treatment for metastatic uveal melanoma (UM) and overall outcomes are generally poor for those who develop liver metastasis. We performed a retrospective single-institution chart review on consecutive series of UM patients with liver metastasis who were treated at Thomas Jefferson University Hospital between 1971–1993 (Cohort 1, n = 80), 1998–2007 (Cohort 2, n = 198), and 2008–2017 (Cohort 3, n = 452). In total, 70% of patients in Cohort 1 received only systemic therapies as their treatment modality for liver metastasis, while 98% of patients in Cohort 2 and Cohort 3 received liver-directed treatment either alone or with systemic therapy. Median Mets-to-Death OS was shortest in Cohort 1 (5.3 months, 95% CI: 4.2–7.0), longer in Cohort 2 (13.6 months, 95% CI: 12.2–16.6) and longest in Cohort 3 (17.8 months, 95% CI: 16.6–19.4). Median Eye Tx-to-Death OS was shortest in Cohort 1 (40.8 months, 95% CI: 37.1–56.9), and similar in Cohort 2 (62.6 months, 95% CI: 54.6–71.5) and Cohort 3 (59.4 months, 95% CI: 56.2–64.7). It is speculated that this could be due to the shift of treatment modalities from DTIC-based chemotherapy to liverdirected therapies. Combination of liver-directed and newly developed systemic treatments may further improve the survival of these patients

Signals: I. Survey description

SIGNALS, the Star formation, Ionized Gas, and Nebular Abundances Legacy Survey, is a large observing programme designed to investigate massive star formation and H II regions in a sample of local extended galaxies. The programme will use the imaging Fourier transform spectrograph SITELLE at the Canada–France–Hawaii Telescope. Over 355 h (54.7 nights) have been allocated beginning in fall 2018 for eight consecutive semesters. Once completed, SIGNALS will provide a statistically reliable laboratory to investigate massive star formation, including over 50 000 resolved H II regions: the largest, most complete, and homogeneous data base of spectroscopically and spatially resolved extragalactic H II regions ever assembled. For each field observed, three datacubes covering the spectral bands of the filters SN1 (363–386 nm), SN2 (482–513 nm), and SN3 (647–685 nm) are gathered. The spectral resolution selected for each spectral band is 1000, 1000, and 5000, respectively. As defined, the project sample will facilitate the study of small-scale nebular physics and many other phenomena linked to star formation at a mean spatial resolution of ∼20 pc. This survey also has considerable legacy value for additional topics, including planetary nebulae, diffuse ionized gas, and supernova remnants. The purpose of this paper is to present a general outlook of the survey, notably the observing strategy, galaxy sample, and science requirements

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -- most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100\% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science

Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche.

Age at menarche is a marker of timing of puberty in females. It varies widely between individuals, is a heritable trait and is associated with risks for obesity, type 2 diabetes, cardiovascular disease, breast cancer and all-cause mortality. Studies of rare human disorders of puberty and animal models point to a complex hypothalamic-pituitary-hormonal regulation, but the mechanisms that determine pubertal timing and underlie its links to disease risk remain unclear. Here, using genome-wide and custom-genotyping arrays in up to 182,416 women of European descent from 57 studies, we found robust evidence (P < 5 × 10(-8)) for 123 signals at 106 genomic loci associated with age at menarche. Many loci were associated with other pubertal traits in both sexes, and there was substantial overlap with genes implicated in body mass index and various diseases, including rare disorders of puberty. Menarche signals were enriched in imprinted regions, with three loci (DLK1-WDR25, MKRN3-MAGEL2 and KCNK9) demonstrating parent-of-origin-specific associations concordant with known parental expression patterns. Pathway analyses implicated nuclear hormone receptors, particularly retinoic acid and γ-aminobutyric acid-B2 receptor signalling, among novel mechanisms that regulate pubertal timing in humans. Our findings suggest a genetic architecture involving at least hundreds of common variants in the coordinated timing of the pubertal transition

Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization.

The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain ∼8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings—most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science

Selective Induction of Cell Death in Melanoma Cell Lines through Targeting of Mcl-1 and A1

Melanoma is an often fatal form of skin cancer which is remarkably resistant against radio- and chemotherapy. Even new strategies that target RAS/RAF signaling and display unprecedented efficacy are characterized by resistance mechanisms. The targeting of survival pathways would be an attractive alternative strategy, if tumor-specific cell death can be achieved. Bcl-2 proteins play a central role in regulating survival of tumor cells. In this study, we systematically investigated the relevance of antiapoptotic Bcl-2 proteins, i.e., Bcl-2, Bcl-xL, Bcl-w, Mcl-1, and A1, in melanoma cell lines and non-malignant cells using RNAi. We found that melanoma cells required the presence of specific antiapoptotic Bcl-2 proteins: Inhibition of Mcl-1 and A1 strongly induced cell death in some melanoma cell lines, whereas non-malignant cells, i.e., primary human fibroblasts or keratinocytes were not affected. This specific sensitivity of melanoma cells was further enhanced by the combined inhibition of Mcl-1 and A1 and resulted in 60% to 80% cell death in all melanoma cell lines tested. This treatment was successfully combined with chemotherapy, which killed a substantial proportion of cells that survived Mcl-1 and A1 inhibition. Together, these results identify antiapoptotic proteins on which specifically melanoma cells rely on and, thus, provide a basis for the development of new Bcl-2 protein-targeting therapies

Individual participant data meta-analysis of LR-5 in LI-RADS version 2018 versus revised LI-RADS for hepatocellular carcinoma diagnosis

Background A simplification of the Liver Imaging Reporting and Data System (LI-RADS) version 2018 (v2018), revised LI-RADS (rLI-RADS), has been proposed for imaging-based diagnosis of hepatocellular carcinoma (HCC). Single-site data suggest that rLI-RADS category 5 (rLR-5) improves sensitivity while maintaining positive predictive value (PPV) of the LI-RADS v2018 category 5 (LR-5), which indicates definite HCC. Purpose To compare the diagnostic performance of LI-RADS v2018 and rLI-RADS in a multicenter data set of patients at risk for HCC by performing an individual patient data meta-analysis. Materials and Methods Multiple databases were searched for studies published from January 2014 to January 2022 that evaluated the diagnostic performance of any version of LI-RADS at CT or MRI for diagnosing HCC. An individual patient data meta-analysis method was applied to observations from the identified studies. Quality Assessment of Diagnostic Accuracy Studies version 2 was applied to determine study risk of bias. Observations were categorized according to major features and either LI-RADS v2018 or rLI-RADS assignments. Diagnostic accuracies of category 5 for each system were calculated using generalized linear mixed models and compared using the likelihood ratio test for sensitivity and the Wald test for PPV. Results Twenty-four studies, including 3840 patients and 4727 observations, were analyzed. The median observation size was 19 mm (IQR, 11–30 mm). rLR-5 showed higher sensitivity compared with LR-5 (70.6% [95% CI: 60.7, 78.9] vs 61.3% [95% CI: 45.9, 74.7]; P < .001), with similar PPV (90.7% vs 92.3%; P = .55). In studies with low risk of bias (n = 4; 1031 observations), rLR-5 also achieved a higher sensitivity than LR-5 (72.3% [95% CI: 63.9, 80.1] vs 66.9% [95% CI: 58.2, 74.5]; P = .02), with similar PPV (83.1% vs 88.7%; P = .47). Conclusion rLR-5 achieved a higher sensitivity for identifying HCC than LR-5 while maintaining a comparable PPV at 90% or more, matching the results presented in the original rLI-RADS study

Global potential energy surface for the O2 + N2 interaction. Applications to the collisional, spectroscopic, and thermodynamic properties of the complex

A detailed characterization of the interaction between the most abundant molecules in air is important for the understanding of a variety of phenomena in atmospherical science. A completely {\em ab initio} global potential energy surface (PES) for the O$_2(^3\Sigma^-_g)$ + N$_2(^1\Sigma^+_g)$ interaction is reported for the first time. It has been obtained with the symmetry-adapted perturbation theory utilizing a density functional description of monomers [SAPT(DFT)] extended to treat the interaction involving high-spin open-shell complexes. The computed interaction energies of the complex are in a good agreement with those obtained by using the spin-restricted coupled cluster methodology with singles, doubles and noniterative triple excitations [RCCSD(T)]. A spherical harmonics expansion containing a large number of terms due to the anisotropy of the interaction has been built from the {\em ab initio} data. The radial coefficients of the expansion are matched in the long range with the analytical functions based on the recent {\em ab initio} calculations of the electric properties of the monomers [M. Bartolomei et al., J. Comp. Chem., {\bf 32}, 279 (2011)]. The PES is tested against the second virial coefficient $B(T)$ data and the integral cross sections measured with rotationally hot effusive beams, leading in both cases to a very good agreement. The first bound states of the complex have been computed and relevant spectroscopic features of the interacting complex are reported. A comparison with a previous experimentally derived PES is also provided