Ectopic adrenal tissue in equine gonads: morphofunctional features

n/

QoS-aware offloading policies for serverless functions in the Cloud-to-Edge continuum

Function-as-a-Service (FaaS) paradigm is increasingly attractive to bring the benefits of serverless computing to the edge of the network, besides traditional Cloud data centers. However, FaaS adoption in the emerging Cloud-to-Edge Continuum is challenging, mostly due to geographical distribution and heterogeneous resource availability. This emerging landscape calls for effective strategies to trade off low latency at the edge of the network with Cloud resource richness, taking into account the needs of different functions and users. In this paper, we present QoS-aware offloading policies for serverless functions running in the Cloud-to-Edge continuum. We consider heterogeneous functions and service classes, and aim to maximize utility given a monetary budget for resource usage. Specifically, we introduce a two-level approach, where (i) FaaS nodes rely on a randomized policy to schedule every incoming request according to a set of probability values, and (ii) periodically, a linear programming model is solved to determine the probabilities to use for scheduling. We show by extensive simulation that our approach outperforms alternative approaches in terms of generated utility across multiple scenarios. Moreover, we demonstrate that our solution is computationally efficient and can be adopted in large-scale systems. We also demonstrate the functionality of our approach through a proof-of-concept experiment on an open-source FaaS framework

Current Advances in γδ T Cell-Based Tumor Immunotherapy

γδ T cells are a minor population (~5%) of CD3 T cells in the peripheral blood, but abound in other anatomic sites such as the intestine or the skin. There are two major subsets of γδ T cells: those that express Vd1 gene, paired with different Vγ elements, abound in the intestine and the skin, and recognize the major histocompatibility complex (MHC) class I-related molecules such as MHC class I-related molecule A, MHC class I-related molecule B, and UL16-binding protein expressed on many stressed and tumor cells. Conversely, γδ T cells expressing the Vδ2 gene paired with the Vγ9 chain are the predominant (50-90%) γδ T cell population in the peripheral blood and recognize phosphoantigens (PAgs) derived from the mevalonate pathway of mammalian cells, which is highly active upon infection or tumor transformation. Aminobisphosphonates (n-BPs), which inhibit farnesyl pyrophosphate synthase, a downstream enzyme of the mevalonate pathway, cause accumulation of upstream PAgs and therefore promote γδ T cell activation. γδ T cells have distinctive features that justify their utilization in antitumor immunotherapy: they do not require MHC restriction and are less dependent that aà T cells on co-stimulatory signals, produce cytokines with known antitumor effects as interferon-? and tumor necrosis factor-a and display cytotoxic and antitumor activities in vitro and in mouse models in vivo. Thus, there is interest in the potential application of γδ T cells in tumor immunotherapy, and several small-sized clinical trials have been conducted of γδ T cell-based immunotherapy in different types of cancer after the application of PAgs or n-BPs plus interleukin-2 in vivo or after adoptive transfer of ex vivo-expanded γδ T cells, particularly the Vγ9Vδ2 subset. Results from clinical trials testing the efficacy of any of these two strategies have shown that γδ T cell-based therapy is safe, but long-term clinical results to date are inconsistent. In this review, we will discuss the major achievements and pitfalls of the γδ T cell-based immunotherapy of cancer

FIGARO: reinForcement learnInG mAnagement acRoss the computing cOntinuum

The widespread adoption of Artificial Intelligence applications to analyze data generated by Internet of Things sensors leads to the development of the edge computing paradigm. Deploying applications at the periphery of the network effectively addresses cost and latency concerns associated with cloud computing. However, it generates a highly distributed environment with heterogeneous devices, opening the challenges of how to select resources and place application components. Starting from a state-of-the-art design-time tool, we present in this paper a novel framework based on Reinforcement Learning, named FIGARO (reinForcement learnInG mAnagement acRoss the computing cOntinuum). It handles the runtime adaptation of a computing continuum environment, dealing with the variability of the incoming load and service times. To reduce the training time, we exploit the design-time knowledge, achieving a significant reduction in the violations of the response time constraint

Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting

Objective: To develop an automatic COVID-19 Reporting and Data System (CO-RADS)–based classification in a multi-demographic setting. Methods: This multi-institutional review boards–approved retrospective study included 2720 chest CT scans (mean age, 58 years [range 18–100 years]) from Italian and Russian patients. Three board-certified radiologists from three countries assessed randomly selected subcohorts from each population and provided CO-RADS–based annotations. CT radiomic features were extracted from the selected subcohorts after preprocessing steps like lung lobe segmentation and automatic noise reduction. We compared three machine learning models, logistic regression (LR), multilayer perceptron (MLP), and random forest (RF) for the automated CO-RADS classification. Model evaluation was carried out in two scenarios, first, training on a mixed multi-demographic subcohort and testing on an independent hold-out dataset. In the second scenario, training was done on a single demography and externally validated on the other demography. Results: The overall inter-observer agreement for the CO-RADS scoring between the radiologists was substantial (k = 0.80). Irrespective of the type of validation test scenario, suspected COVID-19 CT scans were identified with an accuracy of 84%. SHapley Additive exPlanations (SHAP) interpretation showed that the “wavelet_(LH)_GLCM_Imc1” feature had a positive impact on COVID prediction both with and without noise reduction. The application of noise reduction improved the overall performance between the classifiers for all types. Conclusion: Using an automated model based on the COVID-19 Reporting and Data System (CO-RADS), we achieved clinically acceptable performance in a multi-demographic setting. This approach can serve as a standardized tool for automated COVID-19 assessment. Keypoints: • Automatic CO-RADS scoring of large-scale multi-demographic chest CTs with mean AUC of 0.93 ± 0.04. • Validation procedure resembles TRIPOD 2b and 3 categories, enhancing the quality of experimental design to test the cross-dataset domain shift between institutions aiding clinical integration. • Identification of COVID-19 pneumonia in the presence of community-acquired pneumonia and other comorbidities with an AUC of 0.92

Relative Sea-Level Rise and Potential Submersion Risk for 2100 on 16 Coastal Plains of the Mediterranean Sea.

The coasts of the Mediterranean Sea are dynamic habitats in which human activities have been conducted for centuries and which feature micro-tidal environments with about 0.40 m of range. For this reason, human settlements are still concentrated along a narrow coastline strip, where any change in the sea level and coastal dynamics may impact anthropic activities. In the frame of the RITMARE and the Copernicus Projects, we analyzed light detection and ranging (LiDAR) and Copernicus Earth Observation data to provide estimates of potential marine submersion for 2100 for 16 small-sized coastal plains located in the Italian peninsula and four Mediterranean countries (France, Spain, Tunisia, Cyprus) all characterized by different geological, tectonic and morphological features. The objective of this multidisciplinary study is to provide the first maps of sea-level rise scenarios for 2100 for the IPCC RCP 8.5 and Rahmstorf (2007) projections for the above affected coastal zones, which are the locations of touristic resorts, railways, airports and heritage sites. On the basis of our model (eustatic projection for 2100, glaciohydrostasy values and tectonic vertical movement), we provide 16 high-definition submersion maps. We estimated a potential loss of land for the above areas of between about 148 km(2)(IPCC-RCP8.5 scenario) and 192 km(2)(Rahmstorf scenario), along a coastline length of about 400 km

Sicilia—silicon carbide detectors for intense luminosity investigations and applications

Silicon carbide (SiC) is a compound semiconductor, which is considered as a possible alternative to silicon for particles and photons detection. Its characteristics make it very promising for the next generation of nuclear and particle physics experiments at high beam luminosity. Silicon Carbide detectors for Intense Luminosity Investigations and Applications (SiCILIA) is a project starting as a collaboration between the Italian National Institute of Nuclear Physics (INFN) and IMM-CNR, aiming at the realization of innovative detection systems based on SiC. In this paper, we discuss the main features of silicon carbide as a material and its potential application in the field of particles and photons detectors, the project structure and the strategies used for the prototype realization, and the first results concerning prototype production and their performance

A Roadmap for HEP Software and Computing R&D for the 2020s

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.Peer reviewe

Serverless Functions in the Cloud-Edge Continuum: Challenges and Opportunities

The Function-as-a-Service (FaaS) paradigm is increasingly adopted for the development of Cloud-native applications, which especially benefit from the seamless scalability and attractive pricing models of serverless deployments. With the continuous emergence of latency-sensitive applications and services, including Internet-of-Things and augmented reality, it is now natural to wonder whether and how the FaaS paradigm can be efficiently exploited in the Cloud-Edge Continuum, where serverless functions may benefit from reduced network delay between their invoking users and the FaaS platform. In this paper, we illustrate the key challenges that must be faced to effectively deploy serverless functions in the Cloud-Edge Continuum and review recent contributions proposed by the research community towards overcoming those challenges. We also discuss the key issues that currently remain unsolved and highlight a few research opportunities for better support of FaaS in the Compute Continuum