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International audience; We use an activity-based transport model to simulate the progression of a virus at the regional scale. We analyse several scenarios corresponding to distinct situations and describing how small initial clusters of infected agents expand and reach a pandemic level. We evaluate the effectiveness of some public restrictions and compare the number of infections with respect to the base-case scenario, where no restrictions are in place. We consider the wearing of masks in public transport and/or in some activities (work, leisure and shopping) and the implementation of a lockdown. Our analysis shows that education, including the primary level, is one of the major activities where infections occur. We find that the wearing of masks in transportation only does not yield important impacts. The lockdown is efficient in containing the spread of the virus but, at the same time, significantly increases the length of the wave (factor of two). This is because the number of agents who are susceptible to be infected remains high. Our analysis uses the murdasp tool specifically designed to process the output of transport models and performs the simulation of the pandemic.

International audience; As the Fourth Industrial Revolution gains momentum and involves a plethora of disruptive technology concepts, such as blockchain, they have infiltrated economies that have only experienced a small portion of their scope, consequences, and applications in their different branches. This research aims to examine the potential uses of blockchain technology within the framework of smart contracts in the insurance sector, notably in the event of a pandemic that results in business interruption. Businesses hardly ever take business interruption insurance into account, particularly in a country similar to Lebanon, where natural disasters and pandemics are scarce. Due to the complexity of the task and the numerous requirements for trust in terms of risk consistency, traditional insurance companies are not interested in offering these kinds of insurance contracts. In this current study, a quantitative study was conducted over 213 businesses in various fields and revealed acceptance and socio-demographic differences in the activity sectors of this potentially ground-breaking solution for a developing country that is undergoing a sanitary and economic crisis. As a result, smart contracts and decentralized finance (DeFi) were proposed in the current research as potential solutions to overcome the Lebanese currency devaluation and high insurance costs.

How can two countries’ trade policies be related to each other? A first possibility is that they are not related at all and that each country’s tariffs are defined under national considerations (the “rock” hypothesis). A second is that each country adapts its tariffs in reaction to what the other does (the “tango” hypothesis). A third is that both countries react to events happening in the rest of the world (the “roll” possibility). This paper examines the determinants of Australia’s and the US’ average tariff levels. Relying on historical data that cover a century (1904 to 2005), the three hypotheses are examined. The results indicate a strong long-run relation between the US and the Australian tariffs. Interrelations are also exhibited, with the US decisions influencing more strongly the Australian tariffs than the opposite. The results are important to assess the sustainability and stability of the regional trade agreements in the Pacific area.

Shamsudeen, M., Padmanaban, R., Cabral, P., & Morgado, P. (2022). Spatio-Temporal Analysis of the Impact of Landscape Changes on Vegetation and Land Surface Temperature over Tamil Nadu. Earth, 3(2), 614-638. https://doi.org/10.3390/earth3020036 ----------------------- This study was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under the project UIDB/04152/2020 (Centro de Investigação em Gestão de Informação (MagIC)). Land-use changes adversely may impact ecological entities and humans by affecting the water cycle, environmental changes, and energy balance at global and regional scales. Like many megaregions in fast emerging countries, Tamil Nadu, one of the largest states and most urbanized (49%) and industrial hubs in India, has experienced extensive landuse and landcover change (LULC). However, the extent and level of landscape changes associated with vegetation health, surface permeability, and Land Surface Temperature (LST) has not yet been quantified. In this study, we employed Random Forest (RF) classification on Landsat imageries from 2000 and 2020. We also computed vegetation health, soil moisture, and LST metrics for two decades from Landsat imageries to delineate the impact of landscape changes in Tamil Nadu using Google Earth Engine (GEE). The level of vegetation health and drought for 2020 was more accurately assessed by combining the Temperature Condition Index (TCI) and Vegetation Condition Index (VCI). A Soil moisture index was subsequently used to identify surface permeability. A 75% expansion in urban areas of Tamil Nadu was detected mainly towards the suburban periphery of major cities between 2000 and 2020. We observed an overall increase in the coverage of urban areas (built-up), while a decrease for vegetated (cropland and forest) areas was observed in Tamil Nadu between 2000 and 2020. The Soil-Adjusted Vegetation Index (SAVI) values showed an extensive decline in surface permeability and the LST values showed an overall increase (from a maximum of 41 °C to 43 °C) of surface temperature in Tamil Nadu’s major cities with the highest upsurge for urban built-up areas between 2000 and 2020. Major cities built-up and non-vegetation areas in Tamil Nadu were depicted as potential drought hotspots. Our results deliver significant metrics for surface permeability, vegetation condition, surface temperature, and drought monitoring and urges the regional planning authorities to address the current status and social-ecological impact of landscape changes and to preserve ecosystem services. publishersversion published

Death of despair; Health inequalities; Public health Muerte de desesperación; Desigualdades en salud; Salud pública Mort de desesperació; Desigualtats en salut; Salut pública Background: There is a lack of consensus on the social determinants of Deaths of Despair (DoD), i.e., an increase in mortality attributed to drug overdose, alcohol-related liver disease, and suicide in the United States (USA) during recent years. The objective of this study was to review the scientific literature on DoD with the purpose of identifying relevant social determinants and inequalities related to these mortality trends. Methods: Scoping review focusing on the period 2015–2022 based on PubMed search. Articles were selected according to the following inclusion criteria: published between 1 January 2000 and 31 October 2021; including empirical data; analyzed DoD including the three causes defined by Case and Deaton; analyzed at least one social determinant; written in English; and studied DoD in the USA context only. Studies were excluded if they only analyzed adolescent populations. We synthesized our findings in a narrative report specifically addressing DoD by economic conditions, occupational hazards, educational level, geographical setting, and race/ethnicity. Results: Seventeen studies were included. Overall, findings identify a progressive increase in deaths attributable to suicide, drug overdose, and alcohol-related liver disease in the USA in the last two decades. The literature concerning DoD and social determinants is relatively scarce and some determinants have been barely studied. However different, however, large inequalities have been identified in the manner in which the causes of death embedded in the concept of DoD affect different subpopulations, particularly African American, and Hispanic populations, but blue collar-whites are also significantly impacted. Low socioeconomic position and education levels and working in jobs with high insecurity, unemployment, and living in rural areas were identified as the most relevant social determinants of DoD. Conclusions: There is a need for further research on the structural and intermediate social determinants of DoD and social mechanisms. Intersectional and systemic approaches are needed to better understand and tackle DoD and related inequalities.

A standardised system of clinical pathways often conflicts with providing patient-centred heterogeneous care. Mental health care organisations are searching for new methods to become responsive towards unique treatment needs. Modularity is a method increasingly suggested to reconcile standardisation and customisation. The aim is to investigate the extent to which modularity can be applied to make clinical pathways in specialist mental health care more flexible in order to stimulate shared decision making (SDM) and thereby customise care processes to patient contexts while maintaining evidence-based standards. Methods consist of literature research and a theory-based case study including document analysis and semi-structured interviews, which were performed at a Dutch specialist mental health care organisation. The results show that in current literature two modularity-based structures are proposed that support flexibility and customisation, i.e., ‘Prototype’ and ‘Menu-based’. This study reveals that departments tend to use the prototype method if they have predictable patient needs, evidence-based methods are available and there is sequency in treatment components. The menu-based method is preferred if there are unpredictable needs, or the evidence needed to create interconnectedness in treatment is lacking. In conclusion, prototype or menu-based methods are both suitable for applying SDM and reaching customisation in practice. The choice is determined by three characteristics: predictability of needs, availability of evidence and the interconnectedness of treatment components.

Abdelaziz, A., Anastasiadou, M., & Castelli, M. (2020). A parallel particle swarm optimisation for selecting optimal virtual machine on cloud environment. Applied Sciences (Switzerland), 10(18), 1-25. [2806]. https://doi.org/10.3390/APP10186538 Cloud computing has a significant role in healthcare services, especially in medical applications. In cloud computing, the best choice of virtual machines (Virtual_Ms) has an essential role in the quality improvement of cloud computing by minimising the execution time of medical queries from stakeholders and maximising utilisation of medicinal resources. Besides, the best choice of Virtual_Ms assists the stakeholders to reduce the total execution time of medical requests through turnaround time and maximise CPU utilisation and waiting time. For that, this paper introduces an optimisation model for medical applications using two distinct intelligent algorithms: genetic algorithm (GA) and parallel particle swarm optimisation (PPSO). In addition, a set of experiments was conducted to provide a competitive study between those two algorithms regarding the execution time, the data processing speed, and the system efficiency. The PPSO algorithm was implemented using the MATLAB tool. The results showed that the PPSO algorithm gives accurate outcomes better than the GA in terms of the execution time of medical queries and efficiency by 3.02% and 37.7%, respectively. Also, the PPSO algorithm has been implemented on the CloudSim package. The results displayed that the PPSO algorithm gives accurate outcomes better than default CloudSim in terms of final implementation time of medicinal queries by 33.3%. Finally, the proposed model outperformed the state-of-the-art methods in the literature review by a range from 13% to 67%. publishersversion published

Tonini, A., Redweik, P., Painho, M., & Castelli, M. (2020). Remote estimation of target height from unmanned aerial vehicle (Uav) images. Remote Sensing, 12(21), 1-24. [3602]. https://doi.org/10.3390/rs12213602 This paper focuses on how the height of a target can be swiftly estimated using images acquired by a digital camera installed into moving platforms, such as unmanned aerial vehicles (UAVs). A pinhole camera model after distortion compensation was considered for this purpose since it does not need extensive processing nor vanishing lines. The pinhole model has been extensively employed for similar purposes in past studies but mainly focusing on fixed camera installations. This study analyzes how to tailor the pinhole model for gimballed cameras mounted into UAVs, considering camera parameters and flight parameters. Moreover, it indicates a solution that foresees correcting only a few needed pixels to limit the processing overload. Finally, an extensive analysis was conducted to define the uncertainty associated with the height estimation. The results of this analysis highlighted interesting relationships between UAV‐to‐target relative distance, camera pose, and height uncertainty that allow practical exploitations of the proposed approach. The model was tested with real data in both controlled and uncontrolled environments, the results confirmed the suitability of the proposed method and outcomes of the uncertainty analysis. Finally, this research can open consumer UAVs to innovative applications for urban surveillance. publishersversion published

Kandel, I., & Castelli, M. (2020). How deeply to fine-tune a convolutional neural network: A case study using a histopathology dataset. Applied Sciences (Switzerland), 10(10), [3359]. https://doi.org/10.3390/APP10103359 Accurate classification of medical images is of great importance for correct disease diagnosis. The automation of medical image classification is of great necessity because it can provide a second opinion or even a better classification in case of a shortage of experienced medical staff. Convolutional neural networks (CNN) were introduced to improve the image classification domain by eliminating the need to manually select which features to use to classify images. Training CNN from scratch requires very large annotated datasets that are scarce in the medical field. Transfer learning of CNN weights from another large non-medical dataset can help overcome the problem of medical image scarcity. Transfer learning consists of fine-tuning CNN layers to suit the new dataset. The main questions when using transfer learning are how deeply to fine-tune the network and what difference in generalization that will make. In this paper, all of the experiments were done on two histopathology datasets using three state-of-the-art architectures to systematically study the effect of block-wise fine-tuning of CNN. Results show that fine-tuning the entire network is not always the best option; especially for shallow networks, alternatively fine-tuning the top blocks can save both time and computational power and produce more robust classifiers. publishersversion published

An accurate short-term load forecasting (STLF) is one of the most critical inputs for power plant units&rsquo, planning commitment. STLF reduces the overall planning uncertainty added by the intermittent production of renewable sources, thus, it helps to minimize the hydrothermal electricity production costs in a power grid. Although there is some research in the field and even several research applications, there is a continual need to improve forecasts. This research proposes a set of machine learning (ML) models to improve the accuracy of 168 h forecasts. The developed models employ features from multiple sources, such as historical load, weather, and holidays. Of the five ML models developed and tested in various load profile contexts, the Extreme Gradient Boosting Regressor (XGBoost) algorithm showed the best results, surpassing previous historical weekly predictions based on neural networks. Additionally, because XGBoost models are based on an ensemble of decision trees, it facilitated the model&rsquo, s interpretation, which provided a relevant additional result, the features&rsquo, importance in the forecasting.

Castelli, M., Groznik, A., & Popovič, A. (2020). Forecasting electricity prices: A machine learning approach. Algorithms, 13(5), 1-16. [119]. https://doi.org/10.3390/A13050119 The electricity market is a complex, evolutionary, and dynamic environment. Forecasting electricity prices is an important issue for all electricity market participants. In this study, we shed light on how to improve electricity price forecasting accuracy through the use of a machine learning technique-namely, a novel genetic programming approach. Drawing on empirical data from the largest EU energy markets, we propose a forecasting model that considers variables related to weather conditions, oil prices, and CO2 coupons and predicts energy prices 24 h ahead. We show that the proposed model provides more accurate predictions of future electricity prices than existing prediction methods. Our important findings will assist the electricity market participants in forecasting future price movements. publishersversion published

A 14-Item Mediterranean Diet Adherence Screener (MEDAS) questionnaire was developed and validated in face-to-face interviews, but not via telephone. The aims of this study were to evaluate the validity and reliability of a telephone-administered version of the MEDAS as well as to validate the Portuguese version of the MEDAS questionnaire. A convenience community-based sample of adults (n = 224) participated in a three-stage survey. First, trained researchers administered MEDAS via a telephone. Second, the Portuguese version of Food Frequency Questionnaire (FFQ), and MEDAS were administered in a semi-structured face-to-face interview. Finally, MEDAS was again administered via telephone. The telephone-administered MEDAS questionnaire was compared with the face-to-face-version using several metrics. The telephone-administered MEDAS was significantly correlated with the face-to-face-administered MEDAS [r = 0.805, p &lt, 0.001, interclass correlation coefficient (ICC) = 0.803, p &lt, 0.001] and showed strong agreement (k = 0.60). The MEDAS scores that were obtained in the first and second telephone interviews were significantly correlated (r = 0.661, p &lt, ICC = 0.639, p &lt, 0.001). The overall agreement between the Portuguese version of MEDAS and the FFQ-derived Mediterranean diet adherence score had a Cohen&rsquo, s k = 0.39. The telephone-administered version of MEDAS is a valid tool for assessing the adherence to the Mediterranean diet and acquiring data for large population-based studies.

Producción Científica, Female family caregivers (FFCs) constitute one of the basic supports of socio-health care for dependence in developed countries. The care provided by FFCs may impact their physical and mental health, negatively affecting their quality of life. In order to alleviate the consequences of providing care on FFCs, the Spanish Public Health System has developed the family caregiver care programme (FCCP) to be applied in primary care (PC) centres. The effectiveness of this programme is limited. To date, the addition of a physical therapeutic exercise (PTE) programme to FCCP has not been evaluated. A randomised multicentre clinical trial was carried out in two PC centres of the Spanish Public Health System. In total, 68 FFCs were recruited. The experimental group (EG) performed the usual FCCP (4 sessions, 6 h) added to a PTE programme (36 sessions in 12 weeks) whereas the control group performed the usual FCCP performed in PC. The experimental treatment improved quality of life (d = 1.17 in physical component summary), subjective burden (d = 2.38), anxiety (d = 1.52), depression (d = 1.37) and health-related physical condition (d = 2.44 in endurance). Differences between the groups (p < 0.05) were clinically relevant in favour of the EG. The experimental treatment generates high levels of satisfaction., Ministerio de Economía, Industria y Competitividad - (Project MTM2017–86061-C2–1-P), Junta de Castilla y León y Fondo Europeo de Desarrollo Regional (FEDER) - (Projects VA005P17 y VA002G18)

The aim of the study was to develop a new method to use tree stem information recorded by harvesters along operative logging in remote sensing-based prediction of forest inventory attributes in mature stands. The reference sample plots were formed from harvester data, using two different tree positions: harvester positions (XYH) in global satellite navigation system and computationally improved harvester head positions (XYHH). Study materials consisted of 158 mature Norway-spruce-dominated stands located in Southern Finland that were clear-cut during 2015–16. Tree attributes were derived from the stem dimensions recorded by the harvester. The forest inventory attributes were compiled for both stands and sample plots generated for stands for four different sample plot sizes (254, 509, 761, and 1018 m2). Prediction models between the harvester-based forest inventory attributes and remote sensing features of sample plots were developed. The stand-level predictions were obtained, and basal-area weighted mean diameter (Dg) and basal-area weighted mean height (Hg) were nearly constant for all model alternatives with relative root-mean-square errors (RMSE) roughly 10–11% and 6–8%, respectively, and minor biases. For basal area (G) and volume (V), using either of the position methods, resulted in roughly similar predictions at best, with approximately 25% relative RMSE and 15% bias. With XYHH positions, the predictions of G and V were nearly independent of the sample plot size within 254–761 m2. Therefore, the harvester-based data can be used as ground truth for remote sensing forest inventory methods. In predicting the forest inventory attributes, it is advisable to utilize harvester head positions (XYHH) and a smallest plot size of 254 m2. Instead, if only harvester positions (XYH) are available, expanding the sample plot size to 761 m2 reaches a similar accuracy to that obtained using XYHH positions, as the larger sample plot moderates the uncertainties when determining the individual tree position.

Decreased leaf moisture content, typically measured as equivalent water thickness (EWT), is an early signal of tree stress caused by drought, disease, or pest insects. We investigated the use of two terrestrial laser scanners (TLSs) employing different wavelengths for improving the understanding how EWT can be retrieved in a laboratory setting. Two wavelengths were examined for normalizing the effects of varying leaf structure and geometry on the measured intensity. The relationship between laser intensity features, using red (690 nm) and shortwave infrared (1550 nm) wavelengths, and the EWT of individual leaves or groups of needles were determined with and without intensity corrections. To account for wrinkles and curvatures of the leaves and needles, a model describing the relationship between incidence angle and backscattered intensity was applied. Additionally, a reflectance model describing both diffuse and specular reflectance was employed to remove the fraction of specular reflectance from backscattered intensity. A strong correlation (R-2 = 0.93, RMSE = 0.004 g/cm(2)) was found between a normalized ratio of the two wavelengths and the measured EWT of samples. The applied intensity correction methods did not significantly improve the results of the study. The backscattered intensity responded to changes in EWT but more investigations are needed to test the suitability of TLSs to retrieve EWT in a forest environment.

Recent advances in Computer Science and the spread of internet connection have allowed specialists to virtualize complex environments on the web and offer further information with realistic exploration experiences. At the same time, the fruition of complex geospatial datasets (point clouds, Building Information Modelling (BIM) models, 2D and 3D models) on the web is still a challenge, because usually it involves the usage of different proprietary software solutions, and the input data need further simplification for computational effort reduction. Moreover, integrating geospatial datasets acquired in different ways with various sensors remains a challenge. An interesting question, in that respect, is how to integrate 3D information in a 3D GIS (Geographic Information System) environment and manage different scales of information in the same application. Integrating a multiscale level of information is currently the first step when it comes to digital twinning. It is needed to properly manage complex urban datasets in digital twins related to the management of the buildings (cadastral management, prevention of natural and anthropogenic hazards, structure monitoring, etc.). Therefore, the current research shows the development of a freely accessible 3D Web navigation model based on open-source technology that allows the visualization of heterogeneous complex geospatial datasets in the same virtual environment. This solution employs JavaScript libraries based on WebGL technology. The model is accessible through web browsers and does not need software installation from the user side. The case study is the new building of the University of Twente—Faculty of Geo-Information (ITC), located in Enschede (the Netherlands). The developed solution allows switching between heterogeneous datasets (point clouds, BIM, 2D and 3D models) at different scales and visualization (indoor first-person navigation, outdoor navigation, urban navigation). This solution could be employed by governmental stakeholders or the private sector to remotely visualize complex datasets on the web in a unique visualization, and take decisions only based on open-source solutions. Furthermore, this system can incorporate underground data or real-time sensor data from the IoT (Internet of Things) for digital twinning tasks.

Magnesium has been listed among the 30 critical raw materials by the European Union. In recent years, many green and sustainable alternative Mg2+ sources have been sought to satisfy the EU’s demand and to avoid mineral ore consumption. In this context, saltwork bitterns, the by-products of solar sea salt production, have attracted much attention thanks to their high Mg2+ concentrations (up to 80 g/L) and low Ca2+ and bicarbonate contents (95% (w/w). This work presents a comprehensive experimental effort of reactive precipitation tests with NaOH solutions at stoichiometric and over-stoichiometric concentrations to: (i) assess the technical feasibility of Mg2+ recovery from real bitterns collected in saltworks of the Trapani district (Italy) and, (ii) for the first time, conduct an extensive purity investigation of the precipitated magnesium hydroxide powders as brucite. This experimental investigation demonstrates the possibility of extracting highly valuable compounds from saltwork bittern waste, embracing the water valorization and resource recovery approach.

Background: In the context of the lockdowns due to COVID-19, e-learning has become the sole tool allowing learning objectives to be achieved successfully. However, for some subjects, training aided only by this type of tool encounters much difficulty, especially because of the experimental nature of such subjects. Aim: to assess the efficiency of a simulator for estimating dose calculation of ultrasound and laser based on surveys and a written test in a group of students. Methods: Surveys conducted voluntarily and anonymously by a group of students enrolled in the subject General Procedures in Physiotherapy I of the undergraduate degree in Physiotherapy. Furthermore, an objective test containing dose calculation problems for ultrasound and laser had to be solved. Prior to the completion of the objective test, the simulator for calculating ultrasound doses was provided to half of the subjects, whilst the other half were provided with the simulator for calculating laser doses, with both of which they were allowed to practice for a whole week. Results: Out of all the students enrolled in the first year of the undergraduate degree in Physiotherapy, a total of 38 students completed the surveys and 44 took part in the test for solving dose calculation problems. The surveys showed that a substantial number of students consider the use of a simulator for learning purposes efficient. This consideration was corroborated: the response times were reduced and the quantifications were the same for ultrasound, and better for laser. Conclusions: the use of a simulator for ultrasound and laser dose calculation is welcomed by a substantial number of students, and also represents a good additional tool when learning problem resolution.

More sustainable waste management in the winery and olive oil industries has become a major challenge. Therefore, waste valorization to obtain value-added products (e.g., polyphenols) is an efficient alternative that contributes to circular approaches and sustainable environmental protection. In this work, an integration scheme was purposed based on sustainable extraction and membrane separation processes, such as nanofiltration (NF) and reverse osmosis (RO), for the recovery of polyphenols from winery and olive mill wastes. Membrane processes were evaluated in a closed-loop system and with a flat-sheet membrane configuration (NF270, NF90, and Duracid as NF membranes, and BW30LE as RO membrane). The separation and concentration efficiency were evaluated in terms of the total polyphenol content (TPC), and by polyphenol families (hydroxybenzoic acids, hydroxycinnamic acids, and flavonoids), using high-performance liquid chromatography. The water trans-membrane flux was dependent on the trans-membrane pressure for the NF and RO processes. NF90 membrane rejected around 91% of TPC for the lees filters extracts while NF270 membrane rejected about 99% of TPC for the olive pomace extracts. Otherwise, RO membranes rejected more than 99.9% of TPC for both types of agri-food wastes. Hence, NF and RO techniques could be used to obtain polyphenol-rich streams, and clean water for reuse purposes.

17 USC 105 interim-entered record; under temporary embargo. Fake news is a serious problem because it misinforms people about important issues. The present study examined belief in false headlines about election fraud after the 2020 U.S. presiden- tial election. Belief in election fraud had dangerous consequences, including the deadly insurrection at the U.S. Capitol in January 2021. In the present study, participants rated the truthfulness of true and false headlines about the election, and then completed individual difference measures eight days after the election. Participants with more conservative ideology, greater presidential approval of the outgoing president, greater endorsement of general conspiracy narratives and poorer cog- nitive reflection demonstrated greater belief in false headlines about election fraud. Additionally, consuming more politically conservative election news was associated with greater belief in false headlines. Identifying the factors related to susceptibility to false claims of election fraud offers a path toward countering the influence of these claims by tailoring interventions aimed at decreasing belief in misinformation and decreasing conspiracy beliefs to those most susceptible. U.S. Government affiliation is unstated in article text.

Lesión renal aguda; Registros electrónicos de datos de salud; Adquirido en el hospital Lesió renal aguda; Registres electrònics de dades de salut; Adquirit a l'Hospital Acute kidney injury; Electronic health data records; Hospital-acquired Background. The current models developed to predict hospital-acquired AKI (HA-AKI) in non-critically ill fail to identify the patients at risk of severe HA-AKI stage 3. Objective. To develop and externally validate a model to predict the individual probability of developing HA-AKI stage 3 through the integration of electronic health databases. Methods. Study set: 165,893 non-critically ill hospitalized patients. Using stepwise logistic regression analyses, including demography, chronic comorbidities, and exposure to risk factors prior to AKI detection, we developed a multivariate model to predict HA-AKI stage 3. This model was then externally validated in 43,569 non-critical patients admitted to the validation center. Results. The incidence of HA-AKI stage 3 in the study set was 0.6%. Among chronic comorbidities, the highest odds ratios were conferred by ischemic heart disease, ischemic cerebrovascular disease, chronic congestive heart failure, chronic obstructive pulmonary disease, chronic kidney disease and liver disease. Among acute complications, the highest odd ratios were associated with acute respiratory failure, major surgery and exposure to nephrotoxic drugs. The model showed an AUC of 0.906 (95% CI 0.904 to 0.908), a sensitivity of 89.1 (95% CI 87.0–91.0) and a specificity of 80.5 (95% CI 80.2–80.7) to predict HA-AKI stage 3, but tended to overestimate the risk at low-risk categories with an adequate goodness-of-fit for all risk categories (Chi2: 16.4, p: 0.034). In the validation set, incidence of HA-AKI stage 3 was 0.62%. The model showed an AUC of 0.861 (95% CI 0.859–0.863), a sensitivity of 83.0 (95% CI 80.5–85.3) and a specificity of 76.5 (95% CI 76.2–76.8) to predict HA-AKI stage 3 with an adequate goodness of fit for all risk categories (Chi2: 15.42, p: 0.052). Conclusions. Our study provides a model that can be used in clinical practice to obtain an accurate dynamic assessment of the individual risk of HA-AKI stage 3 along the hospital stay period in non-critically ill patients. This research received no external funding.

International audience; Multidisciplinary management of worsening heart failure (HF) in the elderly improves survival. To ensure patients have access to adequate care, the current HF and French health authority guidelines advise establishing a clearly defined HF patient pathway. This pathway involves coordinating multiple disciplines to manage decompensating HF. Yet, recent registry data indicate that insufficient numbers of patients receive specialised cardiology care, which increases the risk of rehospitalisation and mortality. The patient pathway in France involves three key stages: presentation with decompensated HF, stabilisation within a hospital setting and transitional care back out into the community. In each of these three phases, HF diagnosis, severity and precipitating factors need to be promptly identified and managed. This is particularly pertinent in older, frail patients who may present with atypical symptoms or coexisting comorbidities and for whom geriatric evaluation may be needed or specific geriatric syndrome management implemented. In the transition phase, multi-professional post-discharge management must be coordinated with community health care professionals. When the patient is discharged, HF medication must be optimised, and patients educated about self-care and monitoring symptoms. This review provides practical guidance to clinicians managing worsening HF in the elderly.

Fall prevention is a key priority in healthcare policies. Multicomponent exercises reduce the risk of falls. The purpose of this study is to describe the relationship between functional performance and falls after following the Otago multicomponent exercise programme and previous falls. A prospective multi-centre intervention study was performed on 498 patients aged over 65 in primary care, with or without a history of previous falls. Sociodemographic, anthropometric and functionality data were collected. The primary outcome was the occurrence of falls, functional performance was measured using the Tinetti, Short Physical Performance Battery and Timed Up and Go tests. Among the patients, 29.7% referred to previous falls. There was a statistically significant (p &lt, 0.001) increase in falls at 6 months (10.1%) and at 12 months (7.6%) among participants with previous falls in the baseline assessment compared to those without. In addition, the existence of previous falls could be considered a risk factor at 6 and 12 months (OR =2.37, p = 0.002, and OR = 1.76, p = 0.046, respectively). With regard to balance and gait, differences between the groups were observed at 6 months in the Tinetti score (p &lt, 0.001) and in the baseline assessment Timed Up and Go score (p &lt, 0.044). Multicomponent exercises improve the fall rate, balance and gait in older people, although this improvement is less in people with previous falls. Earlier intervention and tailoring of exercises in patients with previous falls could help improve outcomes.

The most common reactive material used for the construction of a permeable reactive barrier (PRB) is zero valent iron (ZVI), however, its processing can generate corrosive effects that reduce the efficiency of the barrier. The present study makes a major contribution to understanding new reactive materials as natural and synthetic, easy to obtain, economical and environmentally friendly as possible substitutes for the traditional ZHV to be used as filters in the removal of three transition metals (Zn, Cu, Cd). To assess the ability to remove these pollutants, a series of batch and column tests were carried out at laboratory scale with these materials. Through BACH tests, four of seven substances with a removal percentage higher than 99% were prioritized (cabuya, natural clinoptilolite zeolites, sodium mordenite and mordenite). From this group of substances, column tests were performed where it is evidenced that cabuya fiber presents the lowest absorption time (≈189 h) while natural zeolite mordenite shows the highest time (≈833 h). The latter being the best option for the PRB design. The experimental values were also reproduced by the RETRASO code; through this program, the trend between the observed and simulated values with respect to the best reactive substance was corroborated., Special acknowledgment to Carmine Fallico and Adalgisa Tavolaro for coordinating the technical activities between the Escuela Superior de Chimborazo (ESPOCH), the Universidad Técnica de Ambato (UTA), the Universidad de Calabria (UNICAL), the Consiglio Nazionale di Ricerca (CNR-ITM) and the Universidad Técnica de Cataluña.

30 Pág. Departamento de ​Protección Vegetal (INIA), Bacteria in the genus Xanthomonas infect a wide range of crops and wild plants, with most species responsible for plant diseases that have a global economic and environmental impact on the seed, plant, and food trade. Infections by Xanthomonas spp. cause a wide variety of non-specific symptoms, making their identification difficult. The coexistence of phylogenetically close strains, but drastically different in their phenotype, poses an added challenge to diagnosis. Data on future climate change scenarios predict an increase in the severity of epidemics and a geographical expansion of pathogens, increasing pressure on plant health services. In this context, the effectiveness of integrated disease management strategies strongly depends on the availability of rapid, sensitive, and specific diagnostic methods. The accumulation of genomic information in recent years has facilitated the identification of new DNA markers, a cornerstone for the development of more sensitive and specific methods. Nevertheless, the challenges that the taxonomic complexity of this genus represents in terms of diagnosis together with the fact that within the same bacterial species, groups of strains may interact with distinct host species demonstrate that there is still a long way to go. In this review, we describe and discuss the current molecular-based methods for the diagnosis and detection of regulated Xanthomonas, taxonomic and diversity studies in Xanthomonas and genomic approaches for molecular diagnosis., J.C. (Joana Costa) acknowledges R&D Unit Center for Functional Ecology-Science for People and the Planet (CFE), with reference UIDB/04004/2020, financed by FCT/MCTES through national funds (PIDDAC). V.C. acknowledges support of the University of Catania PIA.CE.RI. 2020–2022 Linea 2-Research Project MEDIT-ECO. J.C. acknowledges support from AEI by RTI2018-096018-R-C31 and FEDER. O.P. and I.R. acknowledge the European Union (ERDF, EAFRD) and Réunion regional council for support. J.F.P. acknowledges support from the Department of Life Sciences and Facility Management of the Zurich University of Applied Sciences (ZHAW) in Wädenswil. D.J.S. and J.G.V. also acknowledge joint funding by a grant from BBSRC, NERC, Defra, and the Scottish Government, under the Strategic Priorities Fund Plant Bacterial Diseases programme under the project ‘Xanthomonas plants diseases: mitigating existing, emerging and future threats to UK agriculture’ (BB/T010916/1 and BB/T010908/1). The APC was funded by COST (European Cooperation in Science and technology).

International audience; The molecular architecture and rheological behavior of linear and branched polybutylene succinate blends have been investigated using size-exclusion chromatography, small-amplitude oscillatory shear and extensional rheometry, in view of their processing using cast and blown extrusion. Dynamic viscoelastic properties indicate that a higher branched polybutylene succinate amount in the blend increases the relaxation time due to an increased long-chain branching degree. Branched polybutylene succinate exhibits pronounced strain hardening under uniaxial elongation, which is known to improve processability. Under extensional flow, the 50/50 wt % blend exhibits the same behavior as linear polybutylene succinate.

Do leaders who build a sense of shared social identity in their teams thereby protect them from the adverse effects of workplace stress? This is a question that the present paper explores by testing the hypothesis that identity leadership contributes to stronger team identification among employees and, through this, is associated with reduced burnout. We tested this model with unique datasets from the Global Identity Leadership Development (GILD) project with participants from all inhabited continents. We compared two datasets from 2016/2017 (n = 5290, 20 countries) and 2020/2021 (n = 7294, 28 countries) and found very similar levels of identity leadership, team identification and burnout across the five years. An inspection of the 2020/2021 data at the onset of and later in the COVID-19 pandemic showed stable identity leadership levels and slightly higher levels of both burnout and team identification. Supporting our hypotheses, we found almost identical indirect effects (2016/2017, b = −0.132, 2020/2021, b = −0.133) across the five-year span in both datasets. Using a subset of n = 111 German participants surveyed over two waves, we found the indirect effect confirmed over time with identity leadership (at T1) predicting team identification and, in turn, burnout, three months later. Finally, we explored whether there could be a “too-much-of-a-good-thing” effect for identity leadership. Speaking against this, we found a u-shaped quadratic effect whereby ratings of identity leadership at the upper end of the distribution were related to even stronger team identification and a stronger indirect effect on reduced burnout.

Heme oxygenase 1 (HO-1) is the rate-limiting enzyme of heme oxidative degradation, generating carbon monoxide (CO), free iron, and biliverdin. HO-1, a stress inducible enzyme, is considered as an anti-oxidative and cytoprotective agent. As many studies suggest, HO-1 is highly expressed in the gastrointestinal tract where it is involved in the response to inflammatory processes, which may lead to several diseases such as pancreatitis, diabetes, fatty liver disease, inflammatory bowel disease, and cancer. In this review, we highlight the pivotal role of HO-1 and its downstream effectors in the development of disorders and their beneficial effects on the maintenance of the gastrointestinal tract health. We also examine clinical trials involving the therapeutic targets derived from HO-1 system for the most common diseases of the digestive system. Yes