Z-score mapping for standardized analysis and reporting of cardiovascular magnetic resonance modified Look-Locker inversion recovery (MOLLI) T1 data: normal behavior and validation in patients with amyloidosis

BACKGROUND: T1 mapping using modified Look-Locker inversion recovery (MOLLI) provides quantitative information on myocardial tissue composition. T1 results differ between sites due to variations in hardware and software equipment, limiting the comparability of results. The aim was to test if Z-scores can be used to compare the results of MOLLI T1 mapping from different cardiovascular magnetic resonance (CMR) platforms. METHODS: First, healthy subjects (n = 15) underwent 11 combinations of native short-axis T1 mapping (four CMR systems from two manufacturers at 1.5 T and 3 T, three MOLLI schemes). Mean and standard deviation (SD) of septal myocardial T1 were derived for each combination. T1 maps were transformed into Z-score maps based on mean and SD values using a prototype post-processing module. Second, Z-score mapping was applied to a validation sample of patients with cardiac amyloidosis at 1.5 T (n = 25) or 3 T (n = 13). RESULTS: In conventional T1 analysis, results were confounded by variations in field strength, MOLLI scheme, and manufacturer-specific system characteristics. Z-score-based analysis yielded consistent results without significant differences between any two of the combinations in part 1 of the study. In the validation sample, Z-score mapping differentiated between patients with cardiac amyloidosis and healthy subjects with the same diagnostic accuracy as standard T1 analysis regardless of field strength. CONCLUSIONS: T1 analysis based on Z-score mapping provides consistent results without significant differences due to field strengths, CMR systems, or MOLLI variants, and detects cardiac amyloidosis with the same diagnostic accuracy as conventional T1 analysis. Z-score mapping provides a means to compare native T1 results acquired with MOLLI across different CMR platforms

Molecular mechanisms and cellular functions of cGAS-STING signalling

The cGAS–STING signalling axis, comprising the synthase for the second messenger cyclic GMP–AMP (cGAS) and the cyclic GMP–AMP receptor stimulator of interferon genes (STING), detects pathogenic DNA to trigger an innate immune reaction involving a strong type I interferon response against microbial infections. Notably however, besides sensing microbial DNA, the DNA sensor cGAS can also be activated by endogenous DNA, including extranuclear chromatin resulting from genotoxic stress and DNA released from mitochondria, placing cGAS–STING as an important axis in autoimmunity, sterile inflammatory responses and cellular senescence. Initial models assumed that co-localization of cGAS and DNA in the cytosol defines the specificity of the pathway for non-self, but recent work revealed that cGAS is also present in the nucleus and at the plasma membrane, and such subcellular compartmentalization was linked to signalling specificity of cGAS. Further confounding the simple view of cGAS–STING signalling as a response mechanism to infectious agents, both cGAS and STING were shown to have additional functions, independent of interferon response. These involve non-catalytic roles of cGAS in regulating DNA repair and signalling via STING to NF-κB and MAPK as well as STING-mediated induction of autophagy and lysosome- dependent cell death. We have also learnt that cGAS dimers can multimerize and undergo liquid–liquid phase separation to form biomolecular condensates that could importantly regulate cGAS activation. Here, we review the molecular mechanisms and cellular functions underlying cGAS–STING activation and signalling, particularly highlighting the newly emerging diversity of this signalling pathway and discussing how the specificity towards normal, damage-induced and infection-associated DNA could be achieved

Entry Abort Determination Using Non-Adaptive Neural Networks for Mars Precision Landers

The 2009 Mars Science Laboratory (MSL) will attempt the first precision landing on Mars using a modified version of the Apollo Earth entry guidance program. The guidance routine, Entry Terminal Point Controller (ETPC), commands the deployment of a supersonic parachute after converging the range to the landing target. For very dispersed cases, ETPC may not converge the range to the target and safely command parachute deployment within Mach number and dynamic pressure constraints. A full-lift up abort can save 85% of these failed trajectories while abandoning the precision landing objective. Though current MSL requirements do not call for an abort capability, an autonomous abort capability may be desired, for this mission or future Mars precision landers, to make the vehicle more robust. The application of artificial neural networks (NNs) as an abort determination technique was evaluated by personnel at the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC). In order to implement an abort, a failed trajectory needs to be recognized in real time. Abort determination is dependent upon several trajectory parameters whose relationships to vehicle survival are not well understood, and yet the lander must be trained to recognize unsafe situations. Artificial neural networks (NNs) provide a way to model these parameters and can provide MSL with the artificial intelligence necessary to independently declare an abort. Using the 2009 Mars Science Laboratory (MSL) mission as a case study, a non-adaptive NN was designed, trained and tested using Monte Carlo simulations of MSL descent and incorporated into ETPC. Neural network theory, the development history of the MSL NN, and initial testing with severe dust storm entry trajectory cases are discussed in Reference 1 and will not be repeated here. That analysis demonstrated that NNs are capable of recognizing failed descent trajectories and can significantly increase the survivability of MSL for very dispersed cases. NN testing was then broadened to evaluate fully dispersed entry trajectories. The NN correctly classified 99.7% of descent trajectories as abort or nonabort and reduced the probability of an unsafe parachute deployment by 83%. This second, broader testing phase is discussed in this paper

Prediction of subjective listening effort from acoustic data with non-intrusive deep models

The effort of listening to spoken language is a highly important perceptive measure for the design of speech enhancement algorithms and hearing-aid processing. In previous research, we proposed a model that quantifies the phoneme output probabilities obtained from a deep neural net (DNN), which resulted in accurate predictions for unseen speech samples. However, high correlations between subjective ratings and model output were observed in known noise types, which is an unrealistic assumption in real-life scenarios. This paper explores non-intrusive listening effort prediction in unseen noisy environments. A set of different noise types are used for training a standard automatic speech recognition (ASR) system. Model predictions are produced by measuring the mean temporal distance of phoneme vectors from the DNN and compared to subjective ratings of hearing-impaired and normal-hearing listener responses group in three databases that cover a variety of noise types and signal enhancement algorithms. We obtain an average correlation of 0.88 and outperform three baseline measures in most conditions

Structural homology screens reveal host-derived poxvirus protein families impacting inflammasome activity

Summary: Viruses acquire host genes via horizontal transfer and can express them to manipulate host biology during infections. Some homologs retain sequence identity, but evolutionary divergence can obscure host origins. We use structural modeling to compare vaccinia virus proteins with metazoan proteomes. We identify vaccinia A47L as a homolog of gasdermins, the executioners of pyroptosis. An X-ray crystal structure of A47 confirms this homology, and cell-based assays reveal that A47 interferes with caspase function. We also identify vaccinia C1L as the product of a cryptic gene fusion event coupling a Bcl-2-related fold with a pyrin domain. C1 associates with components of the inflammasome, a cytosolic innate immune sensor involved in pyroptosis, yet paradoxically enhances inflammasome activity, suggesting differential modulation during infections. Our findings demonstrate the increasing power of structural homology screens to reveal proteins with unique combinations of domains that viruses capture from host genes and combine in unique ways