Morphological and multi-level geometrical descriptor analysis in CT and MRI volumes for automatic pancreas segmentation

Automatic pancreas segmentation in 3D radiological scans is a critical, yet challenging task. As a prerequisite for computer-aided diagnosis (CADx) systems, accurate pancreas segmentation could generate both quantitative and qualitative information towards establishing the severity of a condition, and thus provide additional guidance for therapy planning. Since the pancreas is an organ of high inter-patient anatomical variability, previous segmentation approaches report lower quantitative accuracy scores in comparison to abdominal organs such as the liver or kidneys. This paper presents a novel approach for automatic pancreas segmentation in magnetic resonance imaging (MRI) and computer tomography (CT) scans. This method exploits 3D segmentation that, when coupled with geometrical and morphological characteristics of abdominal tissue, classifies distinct contours in tight pixel-range proximity as “pancreas” or “non-pancreas”. There are three main stages to this approach: (1) identify a major pancreas region and apply contrast enhancement to differentiate between pancreatic and surrounding tissue; (2) perform 3D segmentation via continuous max-flow and min-cuts approach, structured forest edge detection, and a training dataset of annotated pancreata; (3) eliminate non-pancreatic contours from resultant segmentation via morphological operations on area, structure and connectivity between distinct contours. The proposed method is evaluated on a dataset containing 82 CT image volumes, achieving mean Dice Similarity coefficient (DSC) of 79.3 ± 4.4%. Two MRI datasets containing 216 and 132 image volumes are evaluated, achieving mean DSC 79.6 ± 5.7% and 81.6 ± 5.1% respectively. This approach is statistically stable, reflected by lower metrics in standard deviation in comparison to state-of-the-art approaches

Cognitive behaviour analysis based on facial information using depth sensors

Cognitive behaviour analysis is considered of high impor- tance with many innovative applications in a range of sectors including healthcare, education, robotics and entertainment. In healthcare, cogni- tive and emotional behaviour analysis helps to improve the quality of life of patients and their families. Amongst all the different approaches for cognitive behaviour analysis, significant work has been focused on emo- tion analysis through facial expressions using depth and EEG data. Our work introduces an emotion recognition approach using facial expres- sions based on depth data and landmarks. A novel dataset was created that triggers emotions from long or short term memories. This work uses novel features based on a non-linear dimensionality reduction, t-SNE, applied on facial landmarks and depth data. Its performance was eval- uated in a comparative study, proving that our approach outperforms other state-of-the-art features

The Pacific Decadal Oscillation modulates tropical cyclone days on the interannual timescale in the North Pacific Ocean

The North Pacific Ocean is the most active region on our planet in terms of tropical cyclone (TC) activity. These storms are responsible for numerous fatalities and economic damages, affecting the livelihood of those living in the impacted areas. Historically the examination of TCs in the North Pacific Ocean has been performed separately for its two main sub-basins: the West North Pacific and the East North Pacific. Here, we consider the TC activity in the North Pacific as a single basin and examine the climate processes responsible for its number of TC days. We show that the Pacific Decadal Oscillation modulates the number of TC days in the North Pacific Ocean through its connection to the sea surface temperature. The insights from this work will advance the understanding of the climate processes responsible for these storms, and will provide valuable information toward our preparation and adaptation efforts on long timescales

3D CATBraTS: Channel Attention Transformer for Brain Tumour Semantic Segmentation

Brain tumour diagnosis is a challenging task yet crucial for planning treatments to stop or slow the growth of a tumour. In the last decade, there has been a dramatic increase in the use of convolutional neural networks (CNN) for their high performance in the automatic segmentation of tumours in medical images. More recently, Vision Transformer (ViT) has become a central focus of medical imaging for its robustness and efficiency when compared to CNNs. In this paper, we propose a novel 3D transformer named 3D CATBraTS for brain tumour semantic segmentation on magnetic resonance images (MRIs) based on the state-of-the-art Swin transformer with a modified CNN-encoder architecture using residual blocks and a channel attention module. The proposed approach is evaluated on the BraTS 2021 dataset and achieved quantitative measures of the mean Dice similarity coefficient (DSC) that surpasses the current state-of-the-art approaches in the validation phase

Future changes in atmospheric rivers and their implications for winter flooding in Britain

Within the warm conveyor belt of extra-tropical cyclones, atmospheric rivers (ARs) are the key synoptic features which deliver the majority of poleward water vapour transport, and are associated with episodes of heavy and prolonged rainfall. ARs are responsible for many of the largest winter floods in the mid-latitudes resulting in major socioeconomic losses; for example, the loss from United Kingdom (UK) flooding in summer/winter 2012 is estimated to be about $1.6 billion in damages. Given the well-established link between ARs and peak river flows for the present day, assessing how ARs could respond under future climate projections is of importance in gauging future impacts from flooding. We show that North Atlantic ARs are projected to become stronger and more numerous in the future scenarios of multiple simulations from five state-of-the-art global climate models (GCMs) in the fifth Climate Model Intercomparison Project (CMIP5). The increased water vapour transport in projected ARs implies a greater risk of higher rainfall totals and therefore larger winter floods in Britain, with increased AR frequency leading to more flood episodes. In the high emissions scenario (RCP8.5) for 2074–2099 there is an approximate doubling of AR frequency in the five GCMs. Our results suggest that the projected change in ARs is predominantly a thermodynamic response to warming resulting from anthropogenic radiative forcing

Community-based participatory research to improve life quality and clinical outcomes of patients with breast cancer (DianaWeb in Umbria pilot study)

Introduction Breast cancer (BC) is the most frequent cancer in Europe and the International Agency for Research on Cancer (IARC) has estimated over 460 000 incident cases per year. Survival among patients with BC has increased in the past decades and EUROCARE-5 has estimated a 5-year relative survival rate of 82% for patients diagnosed in 2000-2007. There is growing evidence that lifestyle (such as a diet based on Mediterranean principles associated with moderate physical activity) may influence prognosis of BC; however, this information is not currently available to patients and is not considered in oncology protocols. Only a few epidemiological studies have investigated the role of diet in BC recurrence and metastasis. Methods and analysis DianaWeb is a community-based participatory research dedicated to patients with BC and represents a collaborative effort between participants and research institutions to determine if specified changes in lifestyle would result in improved outcomes in terms of quality of life or survival. The aim of the study is to recruit a large number of participants, to monitor their lifestyle and health status over time, to provide them tips to encourage sustainable lifestyle changes, to analyse clinical outcomes as a function of baseline risk factors and subsequent changes, and to share with patients methodologies and results. DianaWeb uses a specific interactive website (http://www.dianaweb.org/) and, with very few exceptions, all communications will be made through the web. In this paper we describe the pilot study, namely DianaWeb in Umbria. Ethics and dissemination DianaWeb does not interfere with prescribed oncological treatments; rather, it recommends that participants should follow the received prescriptions. The results will be used to plan guidelines for nutrition and physical activity for patients with BC

Parametric experimental tests of steam gasification of pine wood in a fluidized bed reactor

Among Renewable Energy Sources (RES), biomass represent one of the most common and suitable solution in order to contribute to the global energy supply and to reduce greenhouse gases (GHG) emissions. The disposal of some residual biomass, as pruning from pine trees, represent a problem for agricultural and agro-industrial sectors. But if the residual biomass are used for energy production can become a resource. The most suitable energy conversion technology for the above-mentioned biomass is gasification process because the high C/N ratio and the low moisture content, obtained from the analysis. In this work a small-pilot bubbling-bed gasification plant has been designed, constructed and used in order to obtain, from the pine trees pruning, a syngas with low tar and char contents and high hydrogen content. The activities showed here are part of the activities carried out in the European 7FP UNIfHY project. In particular the aim of this work is to develop experimental test on a bench scale steam blown fluidized bed biomass gasifier. These tests will be utilized in future works for the simulations of a pilot scale steam fluidized bed gasifier (100 kWth) fed with different biomass feedstock. The results of the tests include produced gas and tar composition as well gas, tar and char yield. Tests on a bench scale reactor (8 cm I.D.) were carried out varying steam to biomass ratio from 0.5, 0.7 and 1 to 830°C

Sleep disorder, Mediterranean Diet and learning performance among nursing students : InSOMNIA, a cross-sectional study

Introduzione. L'insonnia è definita dall'International Classification of Sleep disorders, International Classification of Diseases e dal Diagnostic and Statistical manual of Mental Disorders, come "esperienza di sonno insufficiente o di scarsa qualità caratterizzato da almeno uno dei seguenti sintomi: difficoltà a iniziare o mantenere il sonno, risveglio precoce, sonno poco ristoratore". In Italia, lo studio Morfeo 1 rileva una prevalenza pari al 20% con il 44% dei soggetti che presenta anche sintomi diurni. La deprivazione cronica di sonno causa disturbi cognitivi che si ripercuotono sulla vita sociale. É noto come anche gli stili di vita incidano, a loro volta, sul sonno. Alcune delle "norme igieniche del sonno" riguardano il fumo, il consumo di caffè e la dieta. La Dieta Mediterranea (DM), con il suo elevato contenuto di triptofano, può influenzare positivamente il sonno e proteggere da stress e ansia. Disegno dello Studio. Scopo dello studio è stato quello di determinare la prevalenza dei disturbi del sonno in studenti di Scienze Infermieristiche dell'Università di Perugia, e valutare come gli stili di vita, le abitudini alimentari, lo stato di salute ed il progresso accademico siano associati a sintomi notturni e diurni determinati da un sonno interrotto. Metodi. Studio cross-sectional. I dati sono stati raccolti utilizzando il questionario "Sleep and Daytime Habits Questionnaire" per valutare i disturbi del sonno ed il questionario PREDIMED per verificare l'aderenza alla DM. Risultati. I risultati mostrano un'associazione statisticamente significativa tra lo score PREDIMED e il BMI (p-value = 0.0127), l'abitudine tabagica (p-value = 0.0125), la qualità della vita (p-value = 0.0480) ed il profitto accademico (p-value = 0.0092). Conclusioni. Lo studio riscontra un'alta prevalenza di disturbi del sonno statisticamente associati alla dieta e ad un basso progresso accademico.Background. The International Classification of Sleep disorders, the International Classification of Diseases and the Diagnostic and Statistical manual of Mental Disorders defines insomnia as an experience of insufficient or poor sleep quality, characterized by at least one of the following symptoms: difficulty in initiating or maintaining sleep, early awakenings and poor restorative sleep. In Italy, the Morfeo 1 study detects a prevalence of 20% of insomnia and a 40% of cases with day-time symptoms. The chronic sleep deprivation is responsible for cognitive disorders with effects on social life. Being common knowledge, lifestyle can also influence sleep. Some of the "sleep hygiene rules" involve a control on smoking, coffee consumption and diet. The Mediterranean Diet (MD), thanks to its high level of tryptophan, has a positive influence on sleep and can protect against stress and anxiety. Study design. The aim of InSOMNIA study was to determine the prevalence of sleep disorders among nursing students of the University of Perugia and, therefore, to evaluate how lifestyle, eating habits, health status and academics performance are linked to night-time and daytime symptoms of the interrupted sleep. Methods. We adopted a cross sectional survey, collecting data from "Sleep and Daytime Habits Questionnaire" to evaluate the sleep disorders and from PREDIMED questionnaire to assess the adherence to MD. Results. We found a statistical significant association between PREDIMED score and BMI (p-value = 0.0127), smoking habit (p-value = 0.0125), quality of life (p-value = 0.0480) and academic progress (p-value = 0.0092). Conclusions. We found a high prevalence of sleep disturbances statistically associated with diet and poor academic progress