eIF4G stimulates the activity of the DEAD box protein eIF4A by a conformational guidance mechanism

The activity of eIF4A, a key player in translation initiation, is regulated by other translation factors through currently unknown mechanisms. Here, we provide the necessary framework to understand the mechanism of eIF4A's regulation by eIF4G. In solution, eIF4A adopts a defined conformation that is different from the crystal structure. Binding of eIF4G induces a ‘half-open' conformation by interactions with both domains, such that the helicase motifs are pre-aligned for activation. A primary interface acts as an anchor for complex formation. We show here that formation of the secondary interface is essential for imposing the ‘half-open' conformation on eIF4A, and it is critical for the functional interaction of eIF4G with eIF4A. Via this bipartite interaction, eIF4G guides the transition of eIF4A between the ‘half-open' and closed conformations, and stimulates its activity by accelerating the rate-limiting step of phosphate release. Subtle changes induced by eIF4G may be amplified by input signals from other translation factors, leading to an efficient regulation of translation initiatio

Select or adjust? How information from early treatment stages boosts the prediction of non-response in internet-based depression treatment

Background Internet-based interventions produce comparable effectiveness rates as face-to-face therapy in treating depression. Still, more than half of patients do not respond to treatment. Machine learning (ML) methods could help to overcome these low response rates by predicting therapy outcomes on an individual level and tailoring treatment accordingly. Few studies implemented ML algorithms in internet-based depression treatment using baseline self-report data, but differing results hinder inferences on clinical practicability. This work compares algorithms using features gathered at baseline or early in treatment in their capability to predict non-response to a 6-week online program targeting depression. Methods Our training and test sample encompassed 1270 and 318 individuals, respectively. We trained random forest algorithms on self-report and process features gathered at baseline and after 2 weeks of treatment. Non-responders were defined as participants not fulfilling the criteria for reliable and clinically significant change on PHQ-9 post-treatment. Our benchmark models were logistic regressions trained on baseline PHQ-9 sum or PHQ-9 early change, using 100 iterations of randomly sampled 80/20 train-test-splits. Results Best performances were reached by our models involving early treatment characteristics (recall: 0.75–0.76; AUC: 0.71–0.77). Therapeutic alliance and early symptom change constituted the most important predictors. Models trained on baseline data were not significantly better than our benchmark. Conclusions Fair accuracies were only attainable by involving information from early treatment stages. In-treatment adaptation, instead of a priori selection, might constitute a more feasible approach for improving response when relying on easily accessible self-report features. Implementation trials are needed to determine clinical usefulness

“La etapa de evolución social en la que nos encontramos ya está fusionada con la inteligencia artificial, mucho más allá de cualquier punto de retorno”. Entrevista al Dr. Martin Hilbert

Martin Hilbert is, without a doubt, one of the most outstanding researchers of recent times. From his multidisciplinary works published in scientific journals of great impact and trajectory, he has made it possible for us to understand the information society more completely. Hilbert has worked for different international organizations and is particularly interested in the role of information or big data in complex social systems. Disciple of Manuel Castells, and currently, he is an associate professor in the Communications Department of the University of California. With this interview, we wanted to delve into the way in which Hilbert sees the impact of digitalization in areas such as economics, research, politics, social sciences and humanity, in general.Martin Hilbert es, sin lugar a dudas, uno de los más destacados investigadores de los últimos tiempos. Desde sus trabajos multidisciplinarios publicados en revistas científicas de gran impacto y trayectoria, nos ha hecho posible entender a la sociedad de la información de manera más íntegra. Hilbert ha trabajado para distintos organismos internacionales y se interesa particularmente en el rol de la información o big data en sistemas sociales complejos. Discípulo de Manuel Castells, y actualmente, es profesor asociado en el Departamento de Comunicaciones de la Universidad de California. Con esta entrevista, hemos querido ahondar en la forma en que Hilbert ve el impacto de la digitalización en ámbitos como la economía, la investigación, la política, las ciencias sociales y la humanidad, en general

Toward Sharing Brain Images: Differentially Private TOF-MRA Images With Segmentation Labels Using Generative Adversarial Networks

Sharing labeled data is crucial to acquire large datasets for various Deep Learning applications. In medical imaging, this is often not feasible due to privacy regulations. Whereas anonymization would be a solution, standard techniques have been shown to be partially reversible. Here, synthetic data using a Generative Adversarial Network (GAN) with differential privacy guarantees could be a solution to ensure the patient's privacy while maintaining the predictive properties of the data. In this study, we implemented a Wasserstein GAN (WGAN) with and without differential privacy guarantees to generate privacy-preserving labeled Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) image patches for brain vessel segmentation. The synthesized image-label pairs were used to train a U-net which was evaluated in terms of the segmentation performance on real patient images from two different datasets. Additionally, the Fréchet Inception Distance (FID) was calculated between the generated images and the real images to assess their similarity. During the evaluation using the U-Net and the FID, we explored the effect of different levels of privacy which was represented by the parameter ϵ. With stricter privacy guarantees, the segmentation performance and the similarity to the real patient images in terms of FID decreased. Our best segmentation model, trained on synthetic and private data, achieved a Dice Similarity Coefficient (DSC) of 0.75 for ϵ = 7.4 compared to 0.84 for ϵ = ∞ in a brain vessel segmentation paradigm (DSC of 0.69 and 0.88 on the second test set, respectively). We identified a threshold of ϵ <5 for which the performance (DSC <0.61) became unstable and not usable. Our synthesized labeled TOF-MRA images with strict privacy guarantees retained predictive properties necessary for segmenting the brain vessels. Although further research is warranted regarding generalizability to other imaging modalities and performance improvement, our results mark an encouraging first step for privacy-preserving data sharing in medical imaging

Point-of-care ultrasound (POCUS) practices in the helicopter emergency medical services in Europe: results of an online survey

Contains fulltext : 238060.pdf (Publisher’s version ) (Open Access)BACKGROUND: The extent to which Point-of-care of ultrasound (POCUS) is used in different European helicopter EMS (HEMS) is unknown. We aimed to study the availability, perception, and future aspects of POCUS in the European HEMS using an online survey. METHOD: A survey about the use of POCUS in HEMS was conducted by a multinational steering expert committee and was carried out from November 30, 2020 to December 30, 2020 via an online web portal. Invitations for participation were sent via email to the medical directors of the European HEMS organizations including two reminding notes. RESULTS: During the study period, 69 participants from 25 countries and 41 different HEMS providers took part in the survey. 96% (n = 66) completed the survey. POCUS was available in 75% (56% always when needed and 19% occasionally) of the responding HEMS organizations. 17% were planning to establish POCUS in the near future. Responders who provided POCUS used it in approximately 15% of the patients. Participants thought that POCUS is important in both trauma and non-trauma-patients (73%, n = 46). The extended focused assessment sonography for trauma (eFAST) protocol (77%) was the most common protocol used. A POCUS credentialing process including documented examinations was requested in less than one third of the HEMS organizations. CONCLUSIONS: The majority of the HEMS organizations in Europe are able to provide different POCUS protocols in their services. The most used POCUS protocols were eFAST, FATE and RUSH. Despite the enthusiasm for POCUS, comprehensive training and clear credentialing processes are not available in about two thirds of the European HEMS organizations. Due to several limitations of this survey further studies are needed to evaluate POCUS in HEMS

eIF4G stimulates the activity of the DEAD box protein eIF4A by a conformational guidance mechanism

The activity of eIF4A, a key player in translation initiation, is regulated by other translation factors through currently unknown mechanisms. Here, we provide the necessary framework to understand the mechanism of eIF4A’s regulation by eIF4G. In solution, eIF4A adopts a defined conformation that is different from the crystal structure. Binding of eIF4G induces a ‘half-open’ conformation by interactions with both domains, such that the helicase motifs are pre-aligned for activation. A primary interface acts as an anchor for complex formation. We show here that formation of the secondary interface is essential for imposing the ‘half-open’ conformation on eIF4A, and it is critical for the functional interaction of eIF4G with eIF4A. Via this bipartite interaction, eIF4G guides the transition of eIF4A between the ‘half-open’ and closed conformations, and stimulates its activity by accelerating the rate-limiting step of phosphate release. Subtle changes induced by eIF4G may be amplified by input signals from other translation factors, leading to an efficient regulation of translation initiation

Mental health care for irregular migrants in Europe: Barriers and how they are overcome

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited