Caveolin-3 differentially orchestrates cholinergic and serotonergic constriction of murine airways

The mechanisms of controlling airway smooth muscle (ASM) tone are of utmost clinical importance as inappropriate constriction is a hallmark in asthma and chronic obstructive pulmonary disease. Receptors for acetylcholine and serotonin, two relevant mediators in this context, appear to be incorporated in specialized, cholesterol-rich domains of the plasma membrane, termed caveolae due to their invaginated shape. The structural protein caveolin-1 partly accounts for anchoring of these receptors. We here determined the role of the other major caveolar protein, caveolin-3 (cav-3), in orchestrating cholinergic and serotonergic ASM responses, utilizing newly generated cav-3 deficient mice. Cav-3 deficiency fully abrogated serotonin-induced constriction of extrapulmonary airways in organ baths while leaving intrapulmonary airways unaffected, as assessed in precision cut lung slices. The selective expression of cav-3 in tracheal, but not intrapulmonary bronchial epithelial cells, revealed by immunohistochemistry, might explain the differential effects of cav-3 deficiency on serotonergic ASM constriction. The cholinergic response of extrapulmonary airways was not altered, whereas a considerable increase was observed in cav-3â -/- intrapulmonary bronchi. Thus, cav-3 differentially organizes serotonergic and cholinergic signaling in ASM through mechanisms that are specific for airways of certain caliber and anatomical position. This may allow for selective and site-specific intervention in hyperreactive states

Copula Ordinal Regression for Joint Estimation of Facial Action Unit Intensity

Joint modeling of the intensity of facial action units (AUs) from face images is challenging due to the large number of AUs (30+) and their intensity levels (6). This is in part due to the lack of suitable models that can efficiently handle such a large number of outputs/classes simultaneously, but also due to the lack of labelled target data. For this reason, majority of the methods proposed so far resort to independent classifiers for the AU intensity. This is suboptimal for at least two reasons: the facial appearance of some AUs changes depending on the intensity of other AUs, and some AUs co-occur more often than others. Encoding this is expected to improve the estimation of target AU intensities, especially in the case of noisy image features, head-pose variations and imbalanced training data. To this end, we introduce a novel modeling framework, Copula Ordinal Regression (COR), that leverages the power of copula functions and CRFs, to detangle the probabilistic modeling of AU dependencies from the marginal modeling of the AU intensity. Consequently, the COR model achieves the joint learning and inference of intensities of multiple AUs, while being computationally tractable. We show on two challenging datasets of naturalistic facial expressions that the proposed approach consistently outperforms (i) independent modeling of AU intensities, and (ii) the state-ofthe-art approach for the target task

Management of patients with COVID-19 in radiology departments, and indications regarding imaging studies : recommendations of the Polish Medical Society of Radiology

The pandemic involving COVID-19 caused by the SARS-CoV-2 coronavirus, due to its severe symptoms and high transmission rate, has gone on to pose a control challenge for healthcare systems all around the world. We present the second version of the Recommendations of the Polish Medical Society of Radiology, presuming that our knowledge on COVID-19 will advance further rapidly, to the extent that further supplementation and modification will prove necessary. These Recommendations involve rules of conduct, procedures, and safety measures that should be introduced in radiology departments, as well as indications for imaging studies

Introduction to methods of modelling information wars as a 21st century threat

Purpose: Using System Dynamics approach together with Lanchester and SIR models for modeling information war. Theoretical considerations. Approach: Due to the theoretical form of conducted research the main research methodso were a literature review and simulations based on developed model. Conclusions: The result of the research is the model of information war based on System Dynamics approach. The model focuses on how socjety wealth and counterdisinformation campaings affect on war efficiency. One of the key conclusion from the simulations results is that one of the main goals of attacking side should be elimination or taking control over public media of attacked one. Practical implications: The model of information war which was developed during the researche, gives a possibility to get new knowledge about war information procesess. It allows to predict causes and effects of disinformation campaings and helps to make proper decisions connected with countermeasuers that are taken. Presented article appoints directions which needs to be explored in connection with information wars. Orginality: Presented researches are pioneerign and in such form on this field was not conducted so far. Using Lanchester equations, connected with epidemic spread model and System Dynamics approach they provide new knowledge about the phenomenon of information war.peer-reviewe

SEWA DB: A rich database for audio-visual emotion and sentiment research in the wild

Natural human-computer interaction and audio-visual human behaviour sensing systems, which would achieve robust performance in-the-wild are more needed than ever as digital devices are becoming indispensable part of our life more and more. Accurately annotated real-world data are the crux in devising such systems. However, existing databases usually consider controlled settings, low demographic variability, and a single task. In this paper, we introduce the SEWA database of more than 2000 minutes of audio-visual data of 398 people coming from six cultures, 50% female, and uniformly spanning the age range of 18 to 65 years old. Subjects were recorded in two different contexts: while watching adverts and while discussing adverts in a video chat. The database includes rich annotations of the recordings in terms of facial landmarks, facial action units (FAU), various vocalisations, mirroring, and continuously valued valence, arousal, liking, agreement, and prototypic examples of (dis)liking. This database aims to be an extremely valuable resource for researchers in affective computing and automatic human sensing and is expected to push forward the research in human behaviour analysis, including cultural studies. Along with the database, we provide extensive baseline experiments for automatic FAU detection and automatic valence, arousal and (dis)liking intensity estimation

Comparing methods for assessment of facial dynamics in patients with major neurocognitive disorders

International audienceAssessing facial dynamics in patients with major neurocogni-tive disorders and specifically with Alzheimers disease (AD) has shown to be highly challenging. Classically such assessment is performed by clinical staff, evaluating verbal and non-verbal language of AD-patients, since they have lost a substantial amount of their cognitive capacity, and hence communication ability. In addition, patients need to communicate important messages, such as discomfort or pain. Automated methods would support the current healthcare system by allowing for telemedicine, i.e., lesser costly and logistically inconvenient examination. In this work we compare methods for assessing facial dynamics such as talking, singing, neutral and smiling in AD-patients, captured during music mnemotherapy sessions. Specifically, we compare 3D Con-vNets, Very Deep Neural Network based Two-Stream ConvNets, as well as Improved Dense Trajectories. We have adapted these methods from prominent action recognition methods and our promising results suggest that the methods generalize well to the context of facial dynamics. The Two-Stream ConvNets in combination with ResNet-152 obtains the best performance on our dataset, capturing well even minor facial dynamics and has thus sparked high interest in the medical community