Protective effect of ethosuximide on hearing in NOD/LtJ mice via blockage of endogenous apoptosis

Purpose: To determine the protective effect of ethosuximide on the hearing of NOD/LtJ mice, and the underlying mechanism of action. Methods: The mice were randomly assigned to control and treatment groups (20 mice per group). Mice in the treatment group were administered ethosuximide intraperitoneally at a dose of 200mg/kg body weight (bwt), while those in the control group received an equivalent dose of saline via the same route. Both groups were subjected to auditory brainstem response (ABR) and distortion product otoacoustic emissions (DPOAE) tests, as well as determination of mRNA expressions of α1G, α1H, α1I, m-calpain, μ-calpain, and caspase-3. Results: At ages of 6 and 9 weeks, ABR values were significantly lower in the treatment group than those in the control group (p < 0.05). At age 3, 6 and 9 weeks, control group DPOAE values were much lower than those in the treatment group. However, at signal frequency of 35344 Hz, DPOAE value was significantly reduced in the treatment group (p < 0.05). There was significant down-regulation in mRNA expressions of α1G, α1H, α1I, m-calpain, μ-calpain and caspase-3, in the treatment group, when compared with the control group (p < 0.05). Conclusion: Ethosuximide delays mice hearing loss and protects their hearing via a mechanism involving blockage of endogenous apoptotic pathways. This mechanism may provide guidance in the search for suitable new drugs. Keywords: Ethosuximide, Endogenous apoptosis, Hearing, Protectio

Anaphylaxis: Focus on Transcription Factor Activity.

Anaphylaxis is a severe allergic reaction, rapid in onset, and can lead to fatal consequences if not promptly treated. The incidence of anaphylaxis has risen at an alarming rate in past decades and continues to rise. Therefore, there is a general interest in understanding the molecular mechanism that leads to an exacerbated response. The main effector cells are mast cells, commonly triggered by stimuli that involve the IgE-dependent or IgE-independent pathway. These signaling pathways converge in the release of proinflammatory mediators, such as histamine, tryptases, prostaglandins, etc., in minutes. The action and cell targets of these proinflammatory mediators are linked to the pathophysiologic consequences observed in this severe allergic reaction. While many molecules are involved in cellular regulation, the expression and regulation of transcription factors involved in the synthesis of proinflammatory mediators and secretory granule homeostasis are of special interest, due to their ability to control gene expression and change phenotype, and they may be key in the severity of the entire reaction. In this review, we will describe our current understanding of the pathophysiology of human anaphylaxis, focusing on the transcription factors' contributions to this systemic hypersensitivity reaction. Host mutation in transcription factor expression, or deregulation of their activity in an anaphylaxis context, will be updated. So far, the risk of anaphylaxis is unpredictable thus, increasing our knowledge of the molecular mechanism that leads and regulates mast cell activity will enable us to improve our understanding of how anaphylaxis can be prevented or treated

The Multifaceted Mas-Related G Protein-Coupled Receptor Member X2 in Allergic Diseases and Beyond

Recent research on mast cell biology has turned its focus on MRGPRX2, a new member of the Mas-related G protein-coupled subfamily of receptors (Mrgprs), originally described in nociceptive neurons of the dorsal root ganglia. MRGPRX2, a member of this group, is present not only in neurons but also in mast cells (MCs), specifically, and potentially in other cells of the immune system, such as basophils and eosinophils. As emerging new functions for this receptor are studied, a variety of both natural and pharmacologic ligands are being uncovered, linked to the ability to induce receptor-mediated MC activation and degranulation. The diversity of these ligands, characterized in their human, mice, or rat homologues, seems to match that of the receptor's interactions. Natural ligands include host defense peptides, basic molecules, and key neuropeptides such as substance P and vasointestinal peptide (known for their role in the transmission of pain and itch) as well as eosinophil granule-derived proteins. Exogenous ligands include MC secretagogues such as compound 48/80 and mastoparan, a component of bee wasp venom, and several peptidergic drugs, among which are members of the quinolone family, neuromuscular blocking agents, morphine, and vancomycin. These discoveries shed light on its capacity as a multifaceted participant in naturally occurring responses within immunity and neural stimulus perception, as in responses at the center of immune pathology. In host defense, the mice Mrgprb2 has been proven to aid mast cells in the detection of peptidic molecules from bacteria and in the release of peptides with antimicrobial activities and other immune mediators. There are several potential actions described for it in tissue homeostasis and repair. In the realm of pathologic response, there is evidence to suggest that this receptor is also involved in chronic inflammation. Furthermore, MRGPRX2 has been linked to the pathophysiology of non-IgE-mediated immediate hypersensitivity drug reactions. Different studies have shown its possible role in other allergic diseases as well, such as asthma, atopic dermatitis, contact dermatitis, and chronic spontaneous urticaria. In this review, we sought to cover its function in physiologic processes and responses, as well as in allergic and nonallergic immune disease

Robust model of fresh jujube soluble solids content with near-infrared (NIR) spectroscopy

A robust partial least square (PLS) calibration model with high accuracy and stability was established for the measurement of soluble solids content (SSC) of fresh jujube using near-infrared (NIR) spectroscopytechnique. Fresh jujube samples were collected in different areas of Taigu and Taiyuan cities, central China in 2008 and 2009. A partial least squares (PLS) calibration model was established based on the NIR spectra of 70 fresh jujube samples collected in 2008. A good calibration result was obtained with correlation coefficient (Rc) of 0.9530 and the root mean square error of calibration (RMSEC) of 0.3951 °Brix. Another PLS calibration model was established based on the NIR spectral of 180 samples collected in 2009; it resulted in the Rc of 0.8536 and the RMSEC of 1.1410 °Brix. It could be seen that the accuracy of established PLS models were different when samples harvested in different years were used for the model calibration. In order to improve the accuracy and robustness of model, different numbers (5, 10, 15, 20, 30 and 40) of samples harvested in 2008 were added to the calibration sample set of the model with samples harvested in 2009, respectively. The established PLS models obtained Rc with the range of 0.8846 to 0.8893 and RMSEC with the range of 1.0248 to 0.9645 °Brix. The obtained results werebetter than the result of the model which was established only with samples harvested in 2009. Moreover, the models established using different numbers of added samples had similar results. Therefore, it was concluded that adding samples from another harvest year could improve the accuracy and robustness of the model for SSC prediction of fresh jujube. The overall results proved that the consideration of samples from different harvest places and years would be useful for establishing an accuracy and robustness spectral model.Keywords: Near-infrared (NIR) spectroscopy, Huping jujube, soluble solids content (SSC), partial least squares (PLS), accuracy, stabilit

xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

Combinatorial features are essential for the success of many commercial models. Manually crafting these features usually comes with high cost due to the variety, volume and velocity of raw data in web-scale systems. Factorization based models, which measure interactions in terms of vector product, can learn patterns of combinatorial features automatically and generalize to unseen features as well. With the great success of deep neural networks (DNNs) in various fields, recently researchers have proposed several DNN-based factorization model to learn both low- and high-order feature interactions. Despite the powerful ability of learning an arbitrary function from data, plain DNNs generate feature interactions implicitly and at the bit-wise level. In this paper, we propose a novel Compressed Interaction Network (CIN), which aims to generate feature interactions in an explicit fashion and at the vector-wise level. We show that the CIN share some functionalities with convolutional neural networks (CNNs) and recurrent neural networks (RNNs). We further combine a CIN and a classical DNN into one unified model, and named this new model eXtreme Deep Factorization Machine (xDeepFM). On one hand, the xDeepFM is able to learn certain bounded-degree feature interactions explicitly; on the other hand, it can learn arbitrary low- and high-order feature interactions implicitly. We conduct comprehensive experiments on three real-world datasets. Our results demonstrate that xDeepFM outperforms state-of-the-art models. We have released the source code of xDeepFM at \url{https://github.com/Leavingseason/xDeepFM}.Comment: 10 page

FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction

In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and the corresponding benchmark to evaluate single-view facial 3D reconstruction. By training on FaceScape data, a novel algorithm is proposed to predict elaborate riggable 3D face models from a single image input. FaceScape dataset provides 18,760 textured 3D faces, captured from 938 subjects and each with 20 specific expressions. The 3D models contain the pore-level facial geometry that is also processed to be topologically uniformed. These fine 3D facial models can be represented as a 3D morphable model for rough shapes and displacement maps for detailed geometry. Taking advantage of the large-scale and high-accuracy dataset, a novel algorithm is further proposed to learn the expression-specific dynamic details using a deep neural network. The learned relationship serves as the foundation of our 3D face prediction system from a single image input. Different than the previous methods, our predicted 3D models are riggable with highly detailed geometry under different expressions. We also use FaceScape data to generate the in-the-wild and in-the-lab benchmark to evaluate recent methods of single-view face reconstruction. The accuracy is reported and analyzed on the dimensions of camera pose and focal length, which provides a faithful and comprehensive evaluation and reveals new challenges. The unprecedented dataset, benchmark, and code have been released to the public for research purpose.Comment: 14 pages, 13 figures, journal extension of FaceScape(CVPR 2020). arXiv admin note: substantial text overlap with arXiv:2003.1398

An unsupervised approach to discover media frames

Media framing refers to highlighting certain aspect of an issue in the news to promote a particular interpretation to the audience. Supervised learning has often been used to recognize frames in news articles, requiring a known pool of frames for a particular issue, which must be identified by communication researchers through thorough manual content analysis. In this work, we devise an unsupervised learning approach to discover the frames in news articles automatically. Given a set of news articles for a given issue, e.g., gun violence, our method first extracts frame elements from these articles using related Wikipedia articles and the Wikipedia category system. It then uses a community detection approach to identify frames from these frame elements. We discuss the effectiveness of our approach by comparing the frames it generates in an unsupervised manner to the domain-expert-derived frames for the issue of gun violence, for which a supervised learning model for frame recognition exists.Published versio

SARS-CoV-2 vaccine excipients polyethylene glycol and trometamol do not induce mast cell degranulation, in an in vitro model for non-IgE-mediated hypersensitivity

The development of vaccines against SARS-CoV2 brought about several challenges, including the management of hypersensitivity reactions to these formulations. The search for underlying mechanisms involved in these adverse events initially focused on excipients which may trigger mast cell activation responses via non-IgE pathways: polyethylene glycol and trometamol. We sought to determine whether these components, in their pure form, were capable of stimulating mast cells directly. To test this hypothesis, we used an in vitro model for non-IgE-mediated activation that has previously shown degranulation responses induced via MRGPRX2 with known drug agonists of the receptor. Human LAD2 mast cells were incubated with different concentrations (1, 10, 50 mg/ml) of trometamol and of purified polyethylene glycol/Macrogol (molecular weights: 2,000, 3,350, 4,000, and 6,000). Mast cell degranulation was assessed using a beta-hexosaminidase read-out. Interestingly, degranulation responses for all reagents tested showed no significant differences from those obtained from the negative control (basal degranulation). Receptor-silencing assays were therefore not conducted. In summary, purified PEG and trometamol did not induce mast cell degranulation in this in vitro model for the study of non-IgE mechanisms of drug hypersensitivity, previously shown to be useful in the investigation of MRGPRX2 ligands. Studies using complete vaccine formulations, lipid conjugates, and receptor gene variants are needed to further clarify mechanisms of vaccine hypersensitivity

The Effect of Traffic on Situation Awareness and Mental Workload: Simulator-Based Study

In the present study, we investigated the effects of the different traffic on the driver’s situation awareness and the mental workload (MWL). The task used in this study was a medium fidelity, 3-dimensional simulation of a driving environment. The simulation required participants to drive the user’s car and perform a real-world driving task. After the simulated driving, participants were asked to complete two tests which assessed their situation awareness (SA). The mental workload measures in this study consisted of the physiological measures and the subjective assessment. Every participant performed two different traffic simulated driving conditions, one was low traffic, the other was high traffic. The results showed that with the increasing of traffic, the driving performance did not worsen, however participant’s mental workload increased, at the same time, the participant’s situation awareness performance deteriorated.Meanwhile, our results also demonstrated that recall-based SA test and recognition-based test was heterogeneous