Evaluating the application of cricoid pressure during rapid sequence induction and intubation

Cricoid pressure is a widely accepted, yet highly questionable maneuver employed by airway management specialists. The function of cricoid pressure is to help prevent gastric regurgitation and pulmonary aspiration when intubating high-risk patients. Although initially well-received by the medical community, the status of cricoid pressure as a standard of care has been challenged by arguments that this procedure is ineffective, unsafe, and generally unfit for clinical practice. Moreover, the lack of a standardized protocol has contributed to significant discrepancies in the way cricoid pressure is applied. A literature analysis reveals insufficient data to determine whether or not cricoid pressure decreases the risk of regurgitation. However, the maneuver can still be deemed effective because of its anatomical basis. Advanced imaging studies affirm the ability of cricoid pressure to occlude the lumen of the postcricoid hypopharynx, physically impeding passage of gastric or esophageal content through the point of compression. An evaluation of cricoid pressure protocol is done in an effort to establish a standardized set of guidelines. Although a general consensus has been reached regarding certain aspects of the maneuver, such as force and timing, further research is required to thoroughly understand its additional intricacies. In the meantime, a cautious approach to applying cricoid pressure is strongly advised

Could the 21-cm absorption be explained by the dark matter suggested by 8^8Be transitions?

The stronger than expected 21-cm absorption was observed by EDGES recently, and another anomaly of $^8$Be transitions would be signatures of new interactions. These two issues may be related to each other, e.g., pseudoscalar $A$ mediated fermionic millicharged dark matter (DM), and the 21-cm absorption could be induced by photon mediated scattering between MeV millicharged DM and hydrogen. This will be explored in this paper. For fermionic millicharged DM $\bar{\chi} \chi$ with masses in a range of $2 m_A < 2 m_{\chi} < 3 m_A$, the p-wave annihilation $\bar{\chi} \chi \to A A$ would be dominant during DM freeze-out. The s-wave annihilation $\bar{\chi} \chi$ $\to A, \gamma$ $\to e^+ e^-$ is tolerant by constraints from CMB and the 21-cm absorption. The millicharged DM can evade constraints from direct detection experiments. The process of $K^+ \to \pi^+ \pi^0$ with the invisible decay $\pi^0 \to \bar{\chi} \chi$ could be employed to search for the millicharged DM, and future high intensity $K^+$ sources, such as NA62, will do the job.Comment: 6 pages, 2 figures, the accepted version, EPJ

A Deep Embedding Model for Co-occurrence Learning

Co-occurrence Data is a common and important information source in many areas, such as the word co-occurrence in the sentences, friends co-occurrence in social networks and products co-occurrence in commercial transaction data, etc, which contains rich correlation and clustering information about the items. In this paper, we study co-occurrence data using a general energy-based probabilistic model, and we analyze three different categories of energy-based model, namely, the $L_1$, $L_2$ and $L_k$ models, which are able to capture different levels of dependency in the co-occurrence data. We also discuss how several typical existing models are related to these three types of energy models, including the Fully Visible Boltzmann Machine (FVBM) ($L_2$), Matrix Factorization ($L_2$), Log-BiLinear (LBL) models ($L_2$), and the Restricted Boltzmann Machine (RBM) model ($L_k$). Then, we propose a Deep Embedding Model (DEM) (an $L_k$ model) from the energy model in a \emph{principled} manner. Furthermore, motivated by the observation that the partition function in the energy model is intractable and the fact that the major objective of modeling the co-occurrence data is to predict using the conditional probability, we apply the \emph{maximum pseudo-likelihood} method to learn DEM. In consequence, the developed model and its learning method naturally avoid the above difficulties and can be easily used to compute the conditional probability in prediction. Interestingly, our method is equivalent to learning a special structured deep neural network using back-propagation and a special sampling strategy, which makes it scalable on large-scale datasets. Finally, in the experiments, we show that the DEM can achieve comparable or better results than state-of-the-art methods on datasets across several application domains