Fluid evolution in CM carbonaceous chondrites tracked through the oxygen isotopic compositions of carbonates

The oxygen isotopic compositions of calcite grains in four CM carbonaceous chondrites have been determined by NanoSIMS, and results reveal that aqueous solutions evolved in a similar manner between parent body regions with different intensities of aqueous alteration. Two types of calcite were identified in Murchison, Mighei, Cold Bokkeveld and LaPaz Icefield 031166 by differences in their petrographic properties and oxygen isotope values. Type 1 calcite occurs as small equant grains that formed by filling of pore spaces in meteorite matrices during the earliest stages of alteration. On average, the type 1 grains have a δ18O of ∼32–36‰ (VSMOW), and Δ17O of between ∼2‰ and −1‰. Most grains of type 2 calcite precipitated after type 1. They contain micropores and inclusions, and have replaced ferromagnesian silicate minerals. Type 2 calcite has an average δ18O of ∼21–24‰ (VSMOW) and a Δ17O of between ∼−1‰ and −3‰. Such consistent isotopic differences between the two calcite types show that they formed in discrete episodes and from solutions whose δ18O and δ17O values had changed by reaction with parent body silicates, as predicted by the closed-system model for aqueous alteration. Temperatures are likely to have increased over the timespan of calcite precipitation, possibly owing to exothermic serpentinisation. The most highly altered CM chondrites commonly contain dolomite in addition to calcite. Dolomite grains in two previously studied CM chondrites have a narrow range in δ18O (∼25–29‰ VSMOW), with Δ17O ∼−1‰ to −3‰. These grains are likely to have precipitated between types 1 and 2 calcite, and in response to a transient heating event and/or a brief increase in fluid magnesium/calcium ratios. In spite of this evidence for localised excursions in temperature and/or solution chemistry, the carbonate oxygen isotope record shows that fluid evolution was comparable between many parent body regions. The CM carbonaceous chondrites studied here therefore sample either several parent bodies with a very similar initial composition and evolution or, more probably, a single C-type asteroid

Increasing the representation accuracy of quantum simulations of chemistry without extra quantum resources

Proposals for near-term experiments in quantum chemistry on quantum computers leverage the ability to target a subset of degrees of freedom containing the essential quantum behavior, sometimes called the active space. This approximation allows one to treat more difficult problems using fewer qubits and lower gate depths than would otherwise be possible. However, while this approximation captures many important qualitative features, it may leave the results wanting in terms of absolute accuracy (basis error) of the representation. In traditional approaches, increasing this accuracy requires increasing the number of qubits and an appropriate increase in circuit depth as well. Here we introduce a technique requiring no additional qubits or circuit depth that is able to remove much of this approximation in favor of additional measurements. The technique is constructed and analyzed theoretically, and some numerical proof of concept calculations are shown. As an example, we show how to achieve the accuracy of a 20 qubit representation using only 4 qubits and a modest number of additional measurements for a simple hydrogen molecule. We close with an outlook on the impact this technique may have on both near-term and fault-tolerant quantum simulations

Stem cell biology and drug discovery

There are many reasons to be interested in stem cells, one of the most prominent being their potential use in finding better drugs to treat human disease. This article focuses on how this may be implemented. Recent advances in the production of reprogrammed adult cells and their regulated differentiation to disease-relevant cells are presented, and diseases that have been modeled using these methods are discussed. Remaining difficulties are highlighted, as are new therapeutic insights that have emerged

A Population-Based Surveillance Study of Shared Genotypes of Escherichia coli Isolates from Retail Meat and Suspected Cases of Urinary Tract Infections.

There is increasing evidence that retail food may serve as a source of Escherichia coli that causes community-acquired urinary tract infections, but the impact of this source in a community is not known. We conducted a prospective, population-based study in one community to examine the frequency of recovery of uropathogenic E. coli genotypes from retail meat samples. We analyzed E. coli isolates from consecutively collected urine samples of patients suspected to have urinary tract infections (UTIs) at a university-affiliated health service and retail meat samples from the same geographic region. We genotyped all E. coli isolates by multilocus sequence typing (MLST) and tested them for antimicrobial susceptibility. From 2016 to 2017, we cultured 233 E. coli isolates from 230 (21%) of 1,087 urine samples and 177 E. coli isolates from 120 (28%) of 427 retail meat samples. Urine samples contained 61 sequence types (STs), and meat samples had 95 STs; 12 STs (ST10, ST38, ST69, ST80, ST88, ST101, ST117, ST131, ST569, ST906, ST1844, and ST2562) were common to both. Thirty-five (81%) of 43 meat isolates among the 12 STs were from poultry. Among 94 isolates in the 12 STs, 26 (60%) of 43 retail meat isolates and 15 (29%) of 51 human isolates were pan-susceptible (P < 0.005). We found that 21% of E. coli isolates from suspected cases of UTIs belonged to STs found in poultry. Poultry may serve as a possible reservoir of uropathogenic E. coli (UPEC). Additional studies are needed to demonstrate transmission pathways of these UPEC genotypes and their food sources.IMPORTANCE Community-acquired urinary tract infection caused by Escherichia coli is one of the most common infectious diseases in the United States, affecting approximately seven million women and costing approximately 11.6 billion dollars annually. In addition, antibiotic resistance among E. coli bacteria causing urinary tract infection continues to increase, which greatly complicates treatment. Identifying sources of uropathogenic E. coli and implementing prevention measures are essential. However, the reservoirs of uropathogenic E. coli have not been well defined. This study demonstrated that poultry sold in retail stores may serve as one possible source of uropathogenic E. coli This finding adds to a growing body of evidence that suggests that urinary tract infection may be a food-borne disease. More research in this area can lead to the development of preventive strategies to control this common and costly infectious disease

Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose

In time-series forecasting, future target values may be affected by both intrinsic and extrinsic effects. When forecasting blood glucose, for example, intrinsic effects can be inferred from the history of the target signal alone (\textit{i.e.} blood glucose), but accurately modeling the impact of extrinsic effects requires auxiliary signals, like the amount of carbohydrates ingested. Standard forecasting techniques often assume that extrinsic and intrinsic effects vary at similar rates. However, when auxiliary signals are generated at a much lower frequency than the target variable (e.g., blood glucose measurements are made every 5 minutes, while meals occur once every few hours), even well-known extrinsic effects (e.g., carbohydrates increase blood glucose) may prove difficult to learn. To better utilize these \textit{sparse but informative variables} (SIVs), we introduce a novel encoder/decoder forecasting approach that accurately learns the per-timepoint effect of the SIV, by (i) isolating it from intrinsic effects and (ii) restricting its learned effect based on domain knowledge. On a simulated dataset pertaining to the task of blood glucose forecasting, when the SIV is accurately recorded our approach outperforms baseline approaches in terms of rMSE (13.07 [95% CI: 11.77,14.16] vs. 14.14 [12.69,15.27]). In the presence of a corrupted SIV, the proposed approach can still result in lower error compared to the baseline but the advantage is reduced as noise increases. By isolating their effects and incorporating domain knowledge, our approach makes it possible to better utilize SIVs in forecasting.Comment: 10 pages, 9 figures, 5 tables, accepted to AAAI2

What Determines the Incidence and Extent of MgII Absorbing Gas Around Galaxies?

We study the connections between on-going star formation, galaxy mass, and extended halo gas, in order to distinguish between starburst-driven outflows and infalling clouds that produce the majority of observed MgII absorbers at large galactic radii (>~ 10 h^{-1} kpc) and to gain insights into halo gas contents around galaxies. We present new measurements of total stellar mass (M_star), H-alpha emission line strength (EW(H-alpha)), and specific star formation rate (sSFR) for the 94 galaxies published in H.-W. Chen et al. (2010). We find that the extent of MgII absorbing gas, R_MgII, scales with M_star and sSFR, following R_MgII \propto M_star^{0.28}\times sSFR^{0.11}. The strong dependence of R_MgII on M_star is most naturally explained, if more massive galaxies possess more extended halos of cool gas and the observed MgII absorbers arise in infalling clouds which will subsequently fuel star formation in the galaxies. The additional scaling relation of R_MgII with sSFR can be understood either as accounting for extra gas supplies due to starburst outflows or as correcting for suppressed cool gas content in high-mass halos. The latter is motivated by the well-known sSFR--M_star} inverse correlation in field galaxies. Our analysis shows that a joint study of galaxies and MgII absorbers along common sightlines provides an empirical characterization of halo gaseous radius versus halo mass. A comparison study of R_MgII around red- and blue-sequence galaxies may provide the first empirical constraint for resolving the physical origin of the observed sSFR--M_star} relation in galaxies.Comment: 6 pages, 3 figures; ApJL in pres

Physical Origin, Evolution and Observational Signature of Diffused Antiworld

The existence of macroscopic regions with antibaryon excess in the baryon asymmetric Universe with general baryon excess is the possible consequence of practically all models of baryosynthesis. Diffusion of matter and antimatter to the border of antimatter domains defines the minimal scale of the antimatter domains surviving to the present time. A model of diffused antiworld is considered, in which the density within the surviving antimatter domains is too low to form gravitationally bound objects. The possibility to test this model by measurements of cosmic gamma ray fluxes is discussed. The expected gamma ray flux is found to be acceptable for modern cosmic gamma ray detectors and for those planned for the near future.Comment: 9 page

TRUST-LAPSE: An Explainable and Actionable Mistrust Scoring Framework for Model Monitoring

Continuous monitoring of trained ML models to determine when their predictions should and should not be trusted is essential for their safe deployment. Such a framework ought to be high-performing, explainable, post-hoc and actionable. We propose TRUST-LAPSE, a "mistrust" scoring framework for continuous model monitoring. We assess the trustworthiness of each input sample's model prediction using a sequence of latent-space embeddings. Specifically, (a) our latent-space mistrust score estimates mistrust using distance metrics (Mahalanobis distance) and similarity metrics (cosine similarity) in the latent-space and (b) our sequential mistrust score determines deviations in correlations over the sequence of past input representations in a non-parametric, sliding-window based algorithm for actionable continuous monitoring. We evaluate TRUST-LAPSE via two downstream tasks: (1) distributionally shifted input detection, and (2) data drift detection. We evaluate across diverse domains - audio and vision using public datasets and further benchmark our approach on challenging, real-world electroencephalograms (EEG) datasets for seizure detection. Our latent-space mistrust scores achieve state-of-the-art results with AUROCs of 84.1 (vision), 73.9 (audio), and 77.1 (clinical EEGs), outperforming baselines by over 10 points. We expose critical failures in popular baselines that remain insensitive to input semantic content, rendering them unfit for real-world model monitoring. We show that our sequential mistrust scores achieve high drift detection rates; over 90% of the streams show < 20% error for all domains. Through extensive qualitative and quantitative evaluations, we show that our mistrust scores are more robust and provide explainability for easy adoption into practice.Comment: Keywords: Mistrust Scores, Latent-Space, Model monitoring, Trustworthy AI, Explainable AI, Semantic-guided A