Multidisciplinary management of dysphagic patient in oncologic head and neck surgery

Aim. Diagnosis, treatment and cure of patients affected by head and neck tumours may be complex and require the organised intervention of many specialists with specific skills and clinical experiences (counseling education and relation with the patient). In this paper we considered the multidisaplinary management of patients affected by head and neck tumors developed by the I ENT division of the University of Turin, evaluating the case reports of about 23 patients followed from January to December 2007. Analysis focused in particular on the pre- and postsurgery information, the speech therapy counseling, the rehabilitation plan management having the aim to avoid anxiety, depression and distress. Methods. The multitask team was composed by an ENT doctor, a speech therapist, a radiotherapist, a gastroenterologist, an anesthetist, some practical nurses, a dentist, a physiatrist, a physiotherapist, a dietician, a nutritionist and a radiologist. A questionnaire about the patient's compliance and the speech therapy management during the pre- and postsurgery period was developed, with the purpose to verify the customer satisfaction. Results. From the data's analysis a high degree of satisfaction for the received information emerged together with the appreciation of the psychological and technical support provided by the specialized personnel involved. Conclusion. A quick recovery often derives from the technical skills of the personnel, but also from the rehabilitation process and patient's willingness to face his/her illness and its functional and emotional consequences

Determining Actual Evapotranspiration Based on Machine Learning and Sinusoidal Approaches Applied to Thermal High-Resolution Remote Sensing Imagery in a Semi-Arid Ecosystem

Evapotranspiration (ET) is key to assess crop water balance and optimize water-use efficiency. To attain sustainability in cropping systems, especially in semi-arid ecosystems, it is necessary to improve methodologies of ET estimation. A method to predict ET is by using land surface temperature (LST) from remote sensing data and applying the Operational Simplified Surface Energy Balance Model (SSEBop). However, to date, LST information from Landsat-8 Thermal Infrared Sensor (TIRS) has a coarser resolution (100 m) and longer revisit time than Sentinel-2, which does not have a thermal infrared sensor, which compromises its use in ET models as SSEBop. Therefore, in the present study we set out to use Sentinel-2 data at a higher spatial-temporal resolution (10 m) to predict ET. Three models were trained using TIRS’ images as training data (100 m) and later used to predict LST at 10 m in the western section of the Copiapó Valley (Chile). The models were built on cubist (Cub) and random forest (RF) algorithms, and a sinusoidal model (Sin). The predicted LSTs were compared with three meteorological stations located in olives, vineyards, and pomegranate orchards. RMSE values for the prediction of LST at 10 m were 7.09 K, 3.91 K, and 3.4 K in Cub, RF, and Sin, respectively. ET estimation from LST in spatial-temporal relation showed that RF was the best overall performance (R2 = 0.710) when contrasted with Landsat, followed by the Sin model (R2 = 0.707). Nonetheless, the Sin model had the lowest RMSE (0.45 mm d−1) and showed the best performance at predicting orchards’ ET. In our discussion, we argue that a simplistic sinusoidal model built on NDVI presents advantages over RF and Cub, which are constrained to the spatial relation of predictors at different study areas. Our study shows how it is possible to downscale Landsat-8 TIRS’ images from 100 m to 10 m to predict ET.</jats:p

Determining Actual Evapotranspiration Based on Machine Learning and Sinusoidal Approaches Applied to Thermal High-Resolution Remote Sensing Imagery in a Semi-Arid Ecosystem

Evapotranspiration (ET) is key to assess crop water balance and optimize water-use efficiency. To attain sustainability in cropping systems, especially in semi-arid ecosystems, it is necessary to improve methodologies of ET estimation. A method to predict ET is by using land surface temperature (LST) from remote sensing data and applying the Operational Simplified Surface Energy Balance Model (SSEBop). However, to date, LST information from Landsat-8 Thermal Infrared Sensor (TIRS) has a coarser resolution (100 m) and longer revisit time than Sentinel-2, which does not have a thermal infrared sensor, which compromises its use in ET models as SSEBop. Therefore, in the present study we set out to use Sentinel-2 data at a higher spatial-temporal resolution (10 m) to predict ET. Three models were trained using TIRS’ images as training data (100 m) and later used to predict LST at 10 m in the western section of the Copiapó Valley (Chile). The models were built on cubist (Cub) and random forest (RF) algorithms, and a sinusoidal model (Sin). The predicted LSTs were compared with three meteorological stations located in olives, vineyards, and pomegranate orchards. RMSE values for the prediction of LST at 10 m were 7.09 K, 3.91 K, and 3.4 K in Cub, RF, and Sin, respectively. ET estimation from LST in spatial-temporal relation showed that RF was the best overall performance (R2 = 0.710) when contrasted with Landsat, followed by the Sin model (R2 = 0.707). Nonetheless, the Sin model had the lowest RMSE (0.45 mm d−1) and showed the best performance at predicting orchards’ ET. In our discussion, we argue that a simplistic sinusoidal model built on NDVI presents advantages over RF and Cub, which are constrained to the spatial relation of predictors at different study areas. Our study shows how it is possible to downscale Landsat-8 TIRS’ images from 100 m to 10 m to predict ET

Effects of Quality and Quantity of Protein Intake for Type 2 Diabetes Mellitus Prevention and Metabolic Control

Purpose of Review: The aim of this review is to evaluate the ideal protein quality and quantity and the dietary composition for the prevention and metabolic control of type 2 diabetes mellitus (T2DM). Introduction: Although some reviews demonstrate the advantages of a diet with a higher protein intake, other reviews have observed that a diet high in carbohydrates, with low-glycaemic index carbohydrates and good fibre intake, is equally effective in improving insulin sensitivity. Methods: Over 2831 articles were screened, and 24 from the last 5&nbsp;years were analysed and summarised for this review, using the protein, diabetes and insulin glucose metabolic keywords in Pubmed in June 2019. Results: Eleven studies demonstrate that a higher consumption of proteins has a positive effect on insulin sensitivity. A higher intake of animal protein seems to be related to an increased risk of T2DM. Four studies show that consumption of meat has a deleterious effect. Higher intake of plant protein and dairy products is associated with a modestly reduced risk. Discussion: Based on the results obtained, for the prevention of T2DM and all disorders related to metabolic syndrome, no ideal dietary composition has yet been found. The advantage of plant protein sources may be related to the foods’ low-glycaemic index due to the high fibre content. However, the right protein quality (animal and plant) and the quantity for T2DM prevention and metabolic control are unclear and need to be investigated with further long-term studies

Adolescentes : conversando la intimidad. Vida cotidiana, sexualidad y masculinidad

Las investigaciones relativas a las identidades masculinas realizadas en los últimos años han revelado que la adolescencia constituye un período privilegiado, tanto para la consolidación de las formas dominantes de masculinidad, como para la búsqueda de propuestas alternativas. Ello orientó varios estudios realizados por un equipo afiatado de investigadores y sus resultados están en la base del trabajo que aquí se presenta

TRANCO: Thermo radiometric normalization of crop observations

Crop type maps are essential for a wide range of applications such as crop monitoring, and yield estimation. In addition, Earth Observation (EO) systems allow robust and timely mapping of the earth’s surface, usually based on time-series. Yet, existing crop type maps are either global at coarse spatial resolution, or have a local or regional scope. The reasons for this gap can be linked to the scarcity of global crop type datasets at field level to train the models, their bias towards the Northern Hemisphere, as well as the limited transferability of existing crop type models across different regions. One of the main limitations on the transferability is driven by the phenological shift of the crops’ radiometric time series detected by Earth Observation (EO) systems, which is mainly induced by the different climates across regions. In this study, we explore the normalization of EO-based wheat time series with the accumulation of Growing Degree Days (GDD): the Thermo-RAdiometric Normalization of Crop Observations (TRANCO) system. The TRANCO system is based on the assumption that crop phenology evolution is mainly driven by temperature accumulation, represented by the accumulated GDD from Start of Season (SOS) to End of Season (EOS) dates, derived from a crop calendar. We tested the proposed method to normalize wheat on a database of globally distributed samples, whose results show a great improvement of the GDD F1 score (0.90) compared to a simpler normalization approach based on Time windows defined from SOS calendars (0.87) and a baseline without normalization (0.83)

Evaluating PlanetScope and UAV Multispectral Data for Monitoring Winter Wheat and Sustainable Fertilization Practices in Mediterranean Agroecosystems

Cereal crops play a critical role in global food security, but their productivity is increasingly threatened by climate change. This study evaluates the feasibility of using PlanetScope satellite imagery and a UAV equipped with the MicaSense RedEdge multispectral imaging sensor in monitoring winter wheat under various fertilizer treatments in a Mediterranean climate. Eleven fertilizer treatments, including organic-mineral fertilizer (OMF) pellets, were tested. The results show that conventional inorganic fertilization provided the highest yield (8618 kg ha&#8315;1), while yields from OMF showed a comparable performance to traditional fertilizers, indicating their potential for sustainable agriculture. PlanetScope data demonstrated moderate accuracy in predicting canopy cover (R2 = 0.68), crop yield (R2 = 0.54), and grain quality parameters such as protein content (R2 = 0.49), starch (R2 = 0.56), and hectoliter weight (R2 = 0.51). However, its coarser resolution limited its ability to capture finer treatment-induced variability. MicaSense, despite its higher spatial resolution, performed poorly in predicting crop components, with R2 values below 0.35 for yield and protein content. This study highlights the complementary use of remote sensing technologies to optimize wheat management and support climate-resilient agriculture through the integration of sustainable fertilization strategies