The Safe and Effective Use of Low-Assurance Predictions in Safety-Critical Systems

The algorithm-design paradigm of algorithms using predictions is explored as a means of incorporating the computations of lower-assurance components (such as machine-learning based ones) into safety-critical systems that must have their correctness validated to very high levels of assurance. The paradigm is applied to two simple example applications that are relevant to the real-time systems community: energy-aware scheduling, and classification using ML-based classifiers in conjunction with more reliable but slower deterministic classifiers. It is shown how algorithms using predictions achieve much-improved performance when the low-assurance computations are correct, at a cost of no more than a slight performance degradation even when they turn out to be completely wrong

Coupled Natural Fusion Enzymes in a Novel Biocatalytic Cascade Convert Fatty Acids to Amines

[Image: see text] Tambjamine YP1 is a pyrrole-containing natural product. Analysis of the enzymes encoded in the Pseudoalteromonas tunicata “tam” biosynthetic gene cluster (BGC) identified a unique di-domain biocatalyst (PtTamH). Sequence and bioinformatic analysis predicts that PtTamH comprises an N-terminal, pyridoxal 5′-phosphate (PLP)-dependent transaminase (TA) domain fused to a NADH-dependent C-terminal thioester reductase (TR) domain. Spectroscopic and chemical analysis revealed that the TA domain binds PLP, utilizes l-Glu as an amine donor, accepts a range of fatty aldehydes (C(7)–C(14) with a preference for C(12)), and produces the corresponding amines. The previously characterized PtTamA from the “tam” BGC is an ATP-dependent, di-domain enzyme comprising a class I adenylation domain fused to an acyl carrier protein (ACP). Since recombinant PtTamA catalyzes the activation and thioesterification of C(12) acid to the holo-ACP domain, we hypothesized that C(12) ACP is the natural substrate for PtTamH. PtTamA and PtTamH were successfully coupled together in a biocatalytic cascade that converts fatty acids (FAs) to amines in one pot. Moreover, a structural model of PtTamH provides insights into how the TA and TR domains are organized. This work not only characterizes the formation of the tambjamine YP1 tail but also suggests that PtTamA and PtTamH could be useful biocatalysts for FA to amine functional group conversion

Photoprotection:extending lessons learned from studying natural sunscreens to the design of artificial sunscreen constituents

Ultrafast pump–probe spectroscopies and computational chemistry unravel the excited state photophysics responsible for the photostability of molecules in natural and commercial sunscreens.</p

Photoprotective Properties of Eumelanin:Computational Insights into the Photophysics of a Catechol:Quinone Heterodimer Model System

Melanins are skin-centered molecular structures that block harmful UV radiation from the sun and help protect chromosomal DNA from UV damage. Understanding the photodynamics of the chromophores that make up eumelanin is therefore paramount. This manuscript presents a multi-reference computational study of the mechanisms responsible for the experimentally observed photostability of a melanin-relevant model heterodimer comprising a catechol (C)–benzoquinone (Q) pair. The present results validate a recently proposed photoinduced intermolecular transfer of an H atom from an OH moiety of C to a carbonyl-oxygen atom of the Q. Photoexcitation of the ground state C:Q heterodimer (which has a π-stacked “sandwich” structure) results in population of a locally excited ππ* state (on Q), which develops increasing charge-transfer (biradical) character as it evolves to a “hinged” minimum energy geometry and drives proton transfer (i.e., net H atom transfer) from C to Q. The study provides further insights into excited state decay mechanisms that could contribute to the photostability afforded by the bulk polymeric structure of eumelanin

The Blanco DECam Bulge Survey (BDBS) VIII: Chemo-kinematics in the southern Galactic bulge from 2.3 million red clump stars with Gaia DR3 proper motions

The Blanco DECam Bulge Survey (BDBS) provides near-ultraviolet to near-infrared photometry for ~250 million unique stars. By combining BDBS photometry with the latest Gaia astrometry, we characterize the chemo-dynamics of red clump stars across the BDBS footprint, using an unprecedented sample size and sky coverage. We construct a sample of ~2.3 million red clump giants in the bulge with photometric metallicities, BDBS photometric distances, and proper motions. We study the kinematics of the red clump stars as a function of sky position and metallicity, by investigating proper motion rotation curves, velocity dispersions, and proper motion correlations across the southern Galactic bulge. We find that metal-poor red clump stars exhibit lower rotation amplitudes, at ~29 km s$^{-1}$ kpc^{-1}. The peak of the angular velocity is ~39 km s^{-1} kpc^{-1} for [Fe/H] ~ -0.2 dex, exhibiting declining rotation at higher [Fe/H]. The velocity dispersion is higher for metal-poor stars, while metal-rich stars show a steeper gradient with Galactic latitude, with a maximum dispersion at low latitudes along the bulge minor axis. Only metal-rich stars ([Fe/H] >~ -0.5 dex) show clear signatures of the bar in their kinematics, while the metal-poor population exhibits isotropic motions with an axisymmetric pattern around Galactic longitude l = 0. This work reports the largest sample of bulge stars with distance, metallicity, and astrometry and shows clear kinematic differences with metallicity. The global kinematics over the bulge agrees with earlier studies. However, we see striking changes with increasing metallicity and for the first time, see kinematic differences for stars with [Fe/H]>-0.5, suggesting that the bar itself may have kinematics that depends on metallicity.Comment: 12 pages, Accepted for publication in A&

Blanco DECam Bulge Survey (BDBS) IV: Metallicity Distributions and Bulge Structure from 2.6 Million Red Clump Stars

We present photometric metallicity measurements for a sample of 2.6 million bulge red clump stars extracted from the Blanco DECam Bulge Survey (BDBS). Similar to previous studies, we find that the bulge exhibits a strong vertical metallicity gradient, and that at least two peaks in the metallicity distribution functions appear at b < -5. We can discern a metal-poor ([Fe/H] ~ -0.3) and metal-rich ([Fe/H] ~ +0.2) abundance distribution that each show clear systematic trends with latitude, and may be best understood by changes in the bulge's star formation/enrichment processes. Both groups exhibit asymmetric tails, and as a result we argue that the proximity of a star to either peak in [Fe/H] space is not necessarily an affirmation of group membership. The metal-poor peak shifts to lower [Fe/H] values at larger distances from the plane while the metal-rich tail truncates. Close to the plane, the metal-rich tail appears broader along the minor axis than in off-axis fields. We also posit that the bulge has two metal-poor populations -- one that belongs to the metal-poor tail of the low latitude and predominantly metal-rich group, and another belonging to the metal-poor group that dominates in the outer bulge. We detect the X-shape structure in fields with |Z| > 0.7 kpc and for stars with [Fe/H] > -0.5. Stars with [Fe/H] < -0.5 may form a spheroidal or "thick bar" distribution while those with [Fe/H] > -0.1 are strongly concentrated near the plane.Comment: 26 pages, 22 figures, accepted for publication in MNRAS; the full data table is very long so only a stub table has been provided here; the full electronic table will be provided through MNRAS upon publication, but early access to the full table will be granted upon request to the author

Loss of AMP-activated protein kinase alpha 2 subunit in mouse beta-cells impairs glucose-stimulated insulin secretion and inhibits their sensitivity to hypoglycaemia

AMPK (AMP-activated protein kinase) signalling plays a key role in whole-body energy homoeostasis, although its precise role in pancreatic β-cell function remains unclear. In the present stusy, we therefore investigated whether AMPK plays a critical function in β-cell glucose sensing and is required for the maintenance of normal glucose homoeostasis. Mice lacking AMPKα2 in β-cells and a population of hypothalamic neurons (RIPCreα2KO mice) and RIPCreα2KO mice lacking AMPKα1 (α1KORIPCreα2KO) globally were assessed for whole-body glucose homoeostasis and insulin secretion. Isolated pancreatic islets from these mice were assessed for glucose-stimulated insulin secretion and gene expression changes. Cultured β-cells were examined electrophysiologically for their electrical responsiveness to hypoglycaemia. RIPCreα2KO mice exhibited glucose intolerance and impaired GSIS (glucose-stimulated insulin secretion) and this was exacerbated in α1KORIPCreα2KO mice. Reduced glucose concentrations failed to completely suppress insulin secretion in islets from RIPCreα2KO and α1KORIPCreα2KO mice, and conversely GSIS was impaired. β-Cells lacking AMPKα2 or expressing a kinase-dead AMPKα2 failed to hyperpolarize in response to low glucose, although KATP (ATP-sensitive potassium) channel function was intact. We could detect no alteration of GLUT2 (glucose transporter 2), glucose uptake or glucokinase that could explain this glucose insensitivity. UCP2 (uncoupling protein 2) expression was reduced in RIPCreα2KO islets and the UCP2 inhibitor genipin suppressed low-glucose-mediated wild-type mouse β-cell hyperpolarization, mimicking the effect of AMPKα2 loss. These results show that AMPKα2 activity is necessary to maintain normal pancreatic β-cell glucose sensing, possibly by maintaining high β-cell levels of UCP2

The Milky Way Bulge extra-tidal star survey: BH 261 (AL 3)

The Milky Way Bulge extra-tidal star survey (MWBest) is a spectroscopic survey with the goal of identifying stripped globular cluster stars from inner Galaxy clusters. In this way, an indication of the fraction of metal-poor bulge stars that originated from globular clusters can be determined. We observed and analyzed stars in and around BH 261, an understudied globular cluster in the bulge. From seven giants within the tidal radius of the cluster, we measured an average heliocentric radial velocity of = -61 +- 2.6 km/s with a radial velocity dispersion of \sigma = 6.1 +- 1.9 km/s. The large velocity dispersion may have arisen from tidal heating in the cluster's orbit about the Galactic center, or because BH 261 has a high dynamical mass as well as a high mass-to-light ratio. From spectra of five giants, we measure an average metallicity of = -1.1 +- 0.2 dex. We also spectroscopically confirm an RR Lyrae star in BH 261, which yields a distance to the cluster of 7.1 +- 0.4~kpc. Stars with 3D velocities and metallicities consistent with BH 261 reaching to ~0.5 degrees from the cluster are identified. A handful of these stars are also consistent with the spatial distribution of that potential debris from models focussing on the most recent disruption of the cluster.Comment: accepted for publication in The Astronomical Journa

Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: the 2019 International Skin Imaging Collaboration Grand Challenge

Previous studies of artificial intelligence (AI) applied to dermatology have shown AI to have higher diagnostic classification accuracy than expert dermatologists; however, these studies did not adequately assess clinically realistic scenarios, such as how AI systems behave when presented with images of disease categories that are not included in the training dataset or images drawn from statistical distributions with significant shifts from training distributions. We aimed to simulate these real-world scenarios and evaluate the effects of image source institution, diagnoses outside of the training set, and other image artifacts on classification accuracy, with the goal of informing clinicians and regulatory agencies about safety and real-world accuracy.We designed a large dermoscopic image classification challenge to quantify the performance of machine learning algorithms for the task of skin cancer classification from dermoscopic images, and how this performance is affected by shifts in statistical distributions of data, disease categories not represented in training datasets, and imaging or lesion artifacts. Factors that might be beneficial to performance, such as clinical metadata and external training data collected by challenge participants, were also evaluated. 25?331 training images collected from two datasets (in Vienna [HAM10000] and Barcelona [BCN20000]) between Jan 1, 2000, and Dec 31, 2018, across eight skin diseases, were provided to challenge participants to design appropriate algorithms. The trained algorithms were then tested for balanced accuracy against the HAM10000 and BCN20000 test datasets and data from countries not included in the training dataset (Turkey, New Zealand, Sweden, and Argentina). Test datasets contained images of all diagnostic categories available in training plus other diagnoses not included in training data (not trained category). We compared the performance of the algorithms against that of 18 dermatologists in a simulated setting that reflected intended clinical use.64 teams submitted 129 state-of-the-art algorithm predictions on a test set of 8238 images. The best performing algorithm achieved 58·8% balanced accuracy on the BCN20000 data, which was designed to better reflect realistic clinical scenarios, compared with 82·0% balanced accuracy on HAM10000, which was used in a previously published benchmark. Shifted statistical distributions and disease categories not included in training data contributed to decreases in accuracy. Image artifacts, including hair, pen markings, ulceration, and imaging source institution, decreased accuracy in a complex manner that varied based on the underlying diagnosis. When comparing algorithms to expert dermatologists (2460 ratings on 1269 images), algorithms performed better than experts in most categories, except for actinic keratoses (similar accuracy on average) and images from categories not included in training data (26% correct for experts vs 6% correct for algorithms, p<0·0001). For the top 25 submitted algorithms, 47·1% of the images from categories not included in training data were misclassified as malignant diagnoses, which would lead to a substantial number of unnecessary biopsies if current state-of-the-art AI technologies were clinically deployed.We have identified specific deficiencies and safety issues in AI diagnostic systems for skin cancer that should be addressed in future diagnostic evaluation protocols to improve safety and reliability in clinical practice