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The first case of Coronavirus Disease 2019 in Italy was detected on February the 20 th in Lombardy region. Since that date, Lombardy has been the most affected Italian region by the epidemic, and its healthcare system underwent a severe overload during the outbreak. From a public health point of view, therefore, it is fundamental to provide healthcare services with tools that can reveal possible new health system stress periods with a certain time anticipation, which is the main aim of the present study. Moreover, the sequence of law decrees to face the epidemic and the large amount of news generated in the population feelings of anxiety and suspicion. Considering this whole complex context, it is easily understandable how people “overcrowded” social media with messages dealing with the pandemic, and emergency numbers were overwhelmed by the calls. Thus, in order to find potential predictors of possible new health system overloads, we analysed data both from Twitter and emergency services comparing them to the daily infected time series at a regional level. Particularly, we performed a wavelet analysis in the time-frequency plane, to finely discriminate over time the anticipation capability of the considered potential predictors. In addition, a cross-correlation analysis has been performed to find a synthetic indicator of the time delay between the predictor and the infected time series. Our results show that Twitter data are more related to social and political dynamics, while the emergency calls trends can be further evaluated as a powerful tool to potentially forecast new stress periods. Since we analysed aggregated regional data, and taking into account also the huge geographical heterogeneity of the epidemic spread, a future perspective would be to conduct the same analysis on a more local basis.

Research in the medical imaging field using deep learning approaches has become progressively contingent. Scientific findings reveal that supervised deep learning methods’ performance heavily depends on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming task. Therefore, most of the freely accessible biomedical image datasets are small-sized. Furthermore, it is challenging to have big-sized medical image datasets due to privacy and legal issues. Consequently, not a small number of supervised deep learning models are prone to overfitting and cannot produce generalized output. One of the most popular methods to mitigate the issue above goes under the name of data augmentation. This technique helps increase training set size by utilizing various transformations and has been publicized to improve the model performance when tested on new data. This article surveyed different data augmentation techniques employed on mammogram images. The article aims to provide insights into basic and deep learning-based augmentation techniques.

Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper’s main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic.

Castiglione D’Adda is one of the municipalities more precociously and severely affected by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) epidemic in Lombardy. With our study we aimed to understand the diffusion of the infection by mass serological screening. We searched for SARS-CoV-2 IgGs in the entire population on a voluntary basis using lateral flow immunochromatographic tests (RICT) on capillary blood (rapid tests). We then performed chemioluminescent serological assays (CLIA) and naso-pharyngeal swabs (NPS) in a randomized representative sample and in each subject with a positive rapid test. Factors associated with RICT IgG positivity were assessed by uni- and multivariate logistic regression models. Out of the 4143 participants, 918 (22·2%) showed RICT IgG positivity. In multivariable analysis, IgG positivity increases with age, with a significant non-linear effect (p = 0·0404). We found 22 positive NPSs out of the 1330 performed. Albeit relevant, the IgG prevalence is lower than expected and suggests that a large part of the population remains susceptible to the infection. The observed differences in prevalence might reflect a different infection susceptibility by age group. A limited persistence of active infections could be found after several weeks after the epidemic peak in the area.

This review aims to take a journey into the transformative impact of artificial intelligence (AI) on positron emission tomography (PET) imaging. To this scope, a broad overview of AI applications in the field of nuclear medicine and a thorough exploration of deep learning (DL) implementations in cancer diagnosis and therapy through PET imaging will be presented. We firstly describe the behind-the-scenes use of AI for image generation, including acquisition (event positioning, noise reduction though time-of-flight estimation and scatter correction), reconstruction (data-driven and model-driven approaches), restoration (supervised and unsupervised methods), and motion correction. Thereafter, we outline the integration of AI into clinical practice through the applications to segmentation, detection and classification, quantification, treatment planning, dosimetry, and radiomics/radiogenomics combined to tumour biological characteristics. Thus, this review seeks to showcase the overarching transformation of the field, ultimately leading to tangible improvements in patient treatment and response assessment. Finally, limitations and ethical considerations of the AI application to PET imaging and future directions of multimodal data mining in this discipline will be briefly discussed, including pressing challenges to the adoption of AI in molecular imaging such as the access to and interoperability of huge amount of data as well as the “black-box” problem, contributing to the ongoing dialogue on the transformative potential of AI in nuclear medicine. Relevance statement AI is rapidly revolutionising the world of medicine, including the fields of radiology and nuclear medicine. In the near future, AI will be used to support healthcare professionals. These advances will lead to improvements in diagnosis, in the assessment of response to treatment, in clinical decision making and in patient management. Key points • Applying AI has the potential to enhance the entire PET imaging pipeline. • AI may support several clinical tasks in both PET diagnosis and prognosis. • Interpreting the relationships between imaging and multiomics data will heavily rely on AI. Graphical Abstract

Human beings usually rely on communication to express their feeling and ideas and to solve disputes among themselves. A major component required for effective communication is language. Language can occur in different forms, including written symbols, gestures, and vocalizations. It is usually essential for all of the communicating parties to be fully conversant with a common language. However, to date this has not been the case between speech-impaired people who use sign language and people who use spoken languages. A number of different studies have pointed out a significant gaps between these two groups which can limit the ease of communication. Therefore, this study aims to develop an efficient deep learning model that can be used to predict British sign language in an attempt to narrow this communication gap between speech-impaired and non-speech-impaired people in the community. Two models were developed in this research, CNN and LSTM, and their performance was evaluated using a multi-class confusion matrix. The CNN model emerged with the highest performance, attaining training and testing accuracies of 98.8% and 97.4%, respectively. In addition, the model achieved average weighted precession and recall of 97% and 96%, respectively. On the other hand, the LSTM model’s performance was quite poor, with the maximum training and testing performance accuracies achieved being 49.4% and 48.7%, respectively. Our research concluded that the CNN model was the best for recognizing and determining British sign language.

To better understand rapid radiation belt losses, this statistical study examines dropouts, defined as a phase‐space density (PSD) decrease by a factor of ≥5${\\ge} 5$within 8 hr. The relationship between dropouts, storm parameters, solar‐wind drivers, geomagnetic indices, and MeV electron precipitation is analyzed. Four years of data from the Van Allen Probes, measuring electron density, the CALorimetric Electron Telescope on the International Space Station, measuring MeV electron precipitation, and solar‐wind/magnetic indices from the OMNI dataset are utilized. Our investigation reveals that electron loss in PSD increases with disturbance intensity. However, about one‐third of dropouts occur during small geomagnetic disturbance periods, some involving precipitation, while approximately 40% of storms do not lead to dropouts. Superposed epoch analysis identifies solar‐wind density and dynamic pressure as the main dropout drivers, while precipitation becomes more likely with higher trapped electron flux and stronger substorms. Dropouts do not require a negative southward magnetic field component. Plain Language Summary The Earth's outer radiation belt traps energetic electrons within its magnetic field, which can suddenly and significantly decrease in response to geomagnetic disturbance during so‐called dropouts. This study investigates the relationship between high‐energy dropouts, storm types and parameters, geomagnetic state, and electron loss into the atmosphere. Data from the Van Allen Probes, which measure electron levels, and the CALorimetric Electron Telescope on the International Space Station, which tracks electron precipitation, are utilized. The findings reveal three common groups of occurrences: storms with dropouts, storms without dropouts, and dropouts without storms. The main driver for dropouts is solar‐wind dynamic pressure. Precipitation is mainly linked to the trapped electron density in the radiation belt and substorms. Key Points Sudden decreases in MeV electron phase‐space density are typically more pronounced during more intense geomagnetic storms Dropout occurrence cannot be predicted by storm intensity or type alone; instead, solar‐wind density is the primary driver MeV electron precipitation is primarily associated with the trapped flux and substorm intensity

Relativistic electron precipitation (REP) refers to the release of high‐energy electrons initially trapped in the outer radiation belt, which then precipitate into Earth's upper atmosphere, contributing significantly to the rapid depletion of radiation belt electron flux. This study presents a statistical analysis of REP observations collected by the Calorimetric Electron Telescope (CALET) experiment aboard the International Space Station from 2015 to the present day. Specifically, the analysis utilizes count rates acquired from the two top scintillators constituting the top charge detector, each sensitive to electrons with energies above 1.5 and 3.4 MeV, respectively. Analysis of CALET data reveals a previously unreported semi‐annual variation in the occurrence of REP events. REP periodicities resemble those observed for trapped electron fluxes in the outer belt. Furthermore, their amplitude follows the overall trend of solar wind high‐speed streams and the solar activity.

With the introduction of superconducting circuits into the field of quantum optics, many experimental demonstrations of the quantum physics of an artificial atom coupled to a single-mode light field have been realized. Engineering such quantum systems offers the opportunity to explore extreme regimes of light-matter interaction that are inaccessible with natural systems. For instance the coupling strength g can be increased until it is comparable with the atomic or mode frequency ω a , m and the atom can be coupled to multiple modes which has always challenged our understanding of light-matter interaction. Here, we experimentally realize a transmon qubit in the ultra-strong coupling regime, reaching coupling ratios of g / ω m  = 0.19 and we measure multi-mode interactions through a hybridization of the qubit up to the fifth mode of the resonator. This is enabled by a qubit with 88% of its capacitance formed by a vacuum-gap capacitance with the center conductor of a coplanar waveguide resonator. In addition to potential applications in quantum information technologies due to its small size, this architecture offers the potential to further explore the regime of multi-mode ultra-strong coupling. Quantum mechanics: Light-matter interaction in the extreme When light couples to an atom, the two exchange quanta of energy at a frequency called the coupling rate. It has been predicted that by increasing this coupling to rates much larger than anything present in nature, “spooky” entangled states of light would appear. A team led by Gary Steele in the Netherlands at the Delft University of Technology has realized extreme coupling rates using man-made superconducting atoms coupled to microwave “light” in electromagnetic resonators. In the experiment, the atom is very strongly coupled to many different modes of the resonator at the same time, a problem which led to long-standing puzzles in quantum mechanics. Studying such engineered quantum atoms may help us better understand the fundamental interaction of light and matter.

ALICE is a general purpose experiment designed to investigate nucleus-nucleus collisions at the CERN Large Hadron Collider (LHC). Located 52 meters underground, with 28 meters of overburden rock, it has also been used to detect the muonic component of the extensive air showers produced by cosmic-ray interactions in the upper atmosphere. A program of cosmic-ray data taking, with specific triggers for atmospheric muons, was started in 2010 in periods when there is no beam circulating in the LHC. Several million events have been recorded to date. The large size and excellent tracking capability of the ALICE Time Projection Chamber are exploited to detect and reconstruct these muons. In this paper the analysis of the multiplicity distribution of the atmospheric muons detected by ALICE between 2010 and 2013 is presented, along with the comparison with Monte Carlo simulations. Special emphasis is given to the study of high multiplicity events containing more than 100 reconstructed muons. The comprehension of the frequency of these events was an unsolved problem since the pioneering studies performed by ALEPH and DELPHI experiments at LEP. In our work the ALICE measurements show that such high multiplicity events demand primary cosmic rays with energy above 1016 eV. Their frequency can be successfully described by assuming a heavy mass composition of primary cosmic rays above this energy and using the most recent interaction models to describe the development of the air shower resulting from the primary interaction.