Tracking internal temperature and structural dynamics during nail penetration of lithium-ion cells

Mechanical abuse of lithium-ion batteries is widely used during testing to induce thermal runaway, characterize associated risks, and expose cell and module vulnerabilities. However, the repeatability of puncture or ‘nail penetration’ tests is a key issue as there is often a high degree of variability in the resulting thermal runaway process. In this work, the failure mechanisms of 18650 cells punctured at different locations and orientations are characterized with respect to their internal structural degradation, and both their internal and surface temperature, all of which are monitored in real time. The initiation and propagation of thermal runaway is visualized via high-speed synchrotron X-ray radiography at 2000 frames per second, and the surface and internal temperatures are recorded via infrared imaging and a thermocouple embedded in the tip of the penetrating nail, respectively. The influence of the nail, as well as how and where it penetrates the cell, on the initiation and propagation of thermal runaway is described and the suitability of this test method for representing in-field failures is discussed

ART: A machine learning Automated Recommendation Tool for synthetic biology

Biology has changed radically in the last two decades, transitioning from a descriptive science into a design science. Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, and fatty acids. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing

STIM2 regulates PKA-dependent phosphorylation and trafficking of AMPARs

STIMs (STIM1 and STIM2 in mammals) are transmembrane proteins that reside in the endoplasmic reticulum (ER) and regulate store-operated Ca2+ entry (SOCE). The function of STIMs in the brain is only beginning to be explored, and the relevance of SOCE in nerve cells is being debated. Here we identify STIM2 as a central organizer of excitatory synapses. STIM2, but not its paralogue STIM1, influences the formation of dendritic spines and shapes basal synaptic transmission in excitatory neurons. We further demonstrate that STIM2 is essential for cAMP/PKA-dependent phosphorylation of the AMPA receptor (AMPAR) subunit GluA1. cAMP triggers rapid migration of STIM2 to ER–plasma membrane (PM) contact sites, enhances recruitment of GluA1 to these ER-PM junctions, and promotes localization of STIM2 in dendritic spines. Both biochemical and imaging data suggest that STIM2 regulates GluA1 phosphorylation by coupling PKA to the AMPAR in a SOCE-independent manner. Consistent with a central role of STIM2 in regulating AMPAR phosphorylation, STIM2 promotes cAMP-dependent surface delivery of GluA1 through combined effects on exocytosis and endocytosis. Collectively our results point to a unique mechanism of synaptic plasticity driven by dynamic assembly of a STIM2 signaling complex at ER-PM contact sites

International Consensus Statement on Rhinology and Allergy: Rhinosinusitis

Background: The 5 years since the publication of the first International Consensus Statement on Allergy and Rhinology: Rhinosinusitis (ICAR‐RS) has witnessed foundational progress in our understanding and treatment of rhinologic disease. These advances are reflected within the more than 40 new topics covered within the ICAR‐RS‐2021 as well as updates to the original 140 topics. This executive summary consolidates the evidence‐based findings of the document. Methods: ICAR‐RS presents over 180 topics in the forms of evidence‐based reviews with recommendations (EBRRs), evidence‐based reviews, and literature reviews. The highest grade structured recommendations of the EBRR sections are summarized in this executive summary. Results: ICAR‐RS‐2021 covers 22 topics regarding the medical management of RS, which are grade A/B and are presented in the executive summary. Additionally, 4 topics regarding the surgical management of RS are grade A/B and are presented in the executive summary. Finally, a comprehensive evidence‐based management algorithm is provided. Conclusion: This ICAR‐RS‐2021 executive summary provides a compilation of the evidence‐based recommendations for medical and surgical treatment of the most common forms of RS

THE USE OF RECURRENT NEURAL NETWORKS IN CONTINUOUS SPEECH RECOGNITION

This chapter describes a use of recurrent neural networks (i.e., feedback is incorporated in the computation) as an acoustic model for continuous speech recognition. The form of the recurrent neural network is described along with an appropriate parameter estimation procedure. For each frame of acoustic data, the recurrent network generates an estimate of the posterior probability of of the possible phones given the observed acoustic signal. The posteriors are then converted into scaled likelihoods and used as the observation probabilities within a conventional decoding paradigm (e.g., Viterbi decoding). The advantages of using recurrent networks are that they require a small number of parameters and provide a fast decoding capability (relative 3 to conventional, large-vocabulary, HMM systems)