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Transplant-associated thrombotic microangiopathy (TA-TMA) is a severe complication following hematopoietic stem cell transplantation (HSCT). No approved treatments are currently available. This study presents real-world data obtained with narsoplimab, a human immunoglobulin G4 monoclonal antibody that inhibits MASP-2, the effector enzyme of the lectin pathway of the complement system. Between January 2018 and August 2023, 20 (13 adult and 7 pediatric) patients diagnosed with TA-TMA received narsoplimab under an ongoing compassionate use program. The diagnosis was based on internationally defined criteria for pediatric and adult patients. Fifteen patients fulfilled the criteria recently established by an international consensus on TA-TMA. Nineteen patients exhibited high-risk characteristics. Thirteen patients (65%) responded to narsoplimab, achieving transfusion independence and significant clinical improvement. The one-hundred-day Overall Survival (OS) post-TA-TMA diagnosis was 70%, and 100% for responders. Narsoplimab proved to be effective and safe in the treatment of high-risk TA-TMA, with no increased infectious complications or other safety signals of concern across all age groups. The high response rates and the encouraging survival outcomes underscore the potential of narsoplimab as a valuable therapeutic option, particularly for high-risk cases.

Semantic segmentation consists of classifying each pixel of an image and constitutes an essential step towards scene recognition and understanding. Deep convolutional encoder–decoder neural networks now constitute state-of-the-art methods in the field of semantic segmentation. The problem of street scenes’ segmentation for automotive applications constitutes an important application field of such networks and introduces a set of imperative exigencies. Since the models need to be executed on self-driving vehicles to make fast decisions in response to a constantly changing environment, they are not only expected to operate reliably but also to process the input images rapidly. In this paper, we explore genetic programming (GP) as a meta-model that combines four different efficiency-oriented networks for the analysis of urban scenes. Notably, we present and examine two approaches. In the first approach, we represent solutions as GP trees that combine networks’ outputs such that each output class’s prediction is obtained through the same meta-model. In the second approach, we propose representing solutions as lists of GP trees, each designed to provide a unique meta-model for a given target class. The main objective is to develop efficient and accurate combination models that could be easily interpreted, therefore allowing gathering some hints on how to improve the existing networks. The experiments performed on the Cityscapes dataset of urban scene images with semantic pixel-wise annotations confirm the effectiveness of the proposed approach. Specifically, our best-performing models improve systems’ generalization ability by approximately 5% compared to traditional ensembles, 30% for the less performing state-of-the-art CNN and show competitive results with respect to state-of-the-art ensembles. Additionally, they are small in size, allow interpretability, and use fewer features due to GP’s automatic feature selection.

Background and objectives: Depression is highly prevalent in older adults, especially in those with dementia. Trazodone, an antidepressant, has shown to be effective in older patients with moderate anxiolytic and hypnotic activity; and a common off-label use is rising for managing behavioral and psychological symptoms of dementia (BPSD). The aim of the study is to comparatively assess the clinical profiles of older patients treated with trazodone or other antidepressants. Methods: This cross-sectional study involved adults aged ≥ 60 years at risk of or affected with COVID-19 enrolled in the GeroCovid Observational study from acute wards, geriatric and dementia-specific outpatient clinics, as well as long-term care facilities (LTCF). Participants were grouped according to the use of trazodone, other antidepressants, or no antidepressant use. Results: Of the 3396 study participants (mean age 80.6 ± 9.1 years; 57.1% females), 10.8% used trazodone and 8.5% others antidepressants. Individuals treated with trazodone were older, more functionally dependent, and had a higher prevalence of dementia and BPSD than those using other antidepressants or no antidepressant use. Logistic regression analyses found that the presence of BPSD was associated with trazodone use (odds ratio (OR) 28.4, 95% confidence interval (CI) 18-44.7 for the outcome trazodone vs no antidepressants use, among participants without depression; OR 2.17, 95% CI 1.05-4.49 for the outcome trazodone vs no antidepressants use, among participants with depression). A cluster analysis of trazodone use identified three clusters: cluster 1 included mainly women, living at home with assistance, multimorbidity, dementia, BPSD, and depression; cluster 2 included mainly institutionalized women, with disabilities, depression, and dementia; cluster 3 included mostly men, often living at home unassisted, with better mobility performance, fewer chronic diseases, dementia, BPSD, and depression. Discussion: The use of trazodo

Remote sensing measurements offer great potential to quantify evapotranspiration (ET) more accurately. This work assesses several remotely-sensed ET datasets at high, medium and coarse scale through a comparison with eddy covariance (EC) measurements. In particular, daily retrievals obtained from the Sen-ET model and the ECOSTRESS ET product, both having resolutions of tens of meters, were considered. In addition, 3-km daily ET estimates derived from SEVIRI were analyzed, together with 8-day MODIS ET composites at 500-m spatial resolution. The remotely-sensed actual ET retrievals at medium and coarse scale were compared with EC observations at five forest and grassland sites over different climate zones in Italy. Additionally, for one of these sites (Monte Bondone), the high-resolution ET estimates within the EC footprint were also evaluated. Sen-ET retrievals demonstrated a remarkable ability to describe EC latent heat fluxes (e.g., Pearson correlation coefficient, R, equal to 0.83, and unbiased root-mean-square-difference, ubRMSD, equal to 0.85 mm/day), and therefore great potential for producing high-resolution ET estimates, while ECOSTRESS data were less performant (e.g., R = 0.46, and ubRMSD = 1.65 mm/day). Despite the spatial differences between EC footprint size and the medium along with coarse scale of remotely-sensed datasets, the latter both showed a good agreement with the measured EC fluxes. The SEVIRI ET, in particular, proved to be significantly suitable for the ET description also at a local scale (at daily time step, average R = 0.84, and average ubRMSD = 0.83 mm/day over the study sites), despite a tendency to underestimate in-situ measurements (negative biases ranging between 0.2 and 0.85 mm/day were observed). This study contributes to the topic of evaluating specific remotely-sensed ET datasets under different conditions. The obtained results generally confirm the value of satellite-derived retrievals for ET estimate and monitoring, as well as th

Background: Increased success in the treatment of hematological cancers contributed to the increase of 5-year survival for most adolescent and young adults (AYAs) with these tumours. However, as 5-year survival increased, it became clear that AYA long-term survivors were at increased risk for severe late effects. Moreover, limited information on long-term cancer impact is available for AYAs, since most studies focused on children and adolescents. We aimed to assess various long-term outcomes on AYA survivors of hematological cancers. Methods: We selected patients diagnosed with a first primary hematological cancer between 1997 and 2006, in the Italian nationwide population-based cohort of AYA cancer survivors (i.e. alive at least 5 years after cancer diagnosis). Long-term outcomes of interest were: second malignant neoplasms (SMNs), hospitalizations and overall mortality. We calculated standardized incidence ratios (SIRs), standardized hospitalization rate ratios (SHRs) and standardized mortality rate ratios (SMRs). To study morbidity patterns over time, we modeled observed incidence rates by fitting flexible parametric models for nonlinear patterns and we used linear regression for linear patterns. Results: The study cohort included 5,042 AYA hematological cancer survivors of which 1,237 and 3,805 had a leukaemia and lymphoma diagnosis, respectively. AYA survivors were at substantially increased risk for SMN (SIR=2.1; 95%CI=1.7; 2.6), hospitalisation (SHR=1.5; 95%CI=1.5; 1.6), and mortality (SMR=1.4; 95%CI=1.2; 1.6) with differences between leukaemia and lymphoma survivors. The highest excess risks of hospitalisations were for infectious diseases, respiratory diseases, and diseases of blood and blood-forming organs. The morbidity pattern differs over time by morbidity type. Conclusions: Our results support the need for strict follow-up plans for survivors, and call for further study to better personalised follow-up plans for AYA cancer survivors.

Many satellite-derived evapotranspiration (ET) estimates rely on coarse resolution (CR) land surface temperature (LST) from 1-km thermal infrared bands offered by NASA and ESA instruments, like Terra MODIS and Sentinel-3 SLSTR. This affects prediction performance of ET, especially in complex regions, such as the Alps. Since most two-source energy balance (TSEB) models assume no thermal variability within a pixel, a major challenge in ET modelling is related to cell grid heterogeneity. Given this limitation, we investigate the potential of kernel-driven downscaling to obtain sub-kilometer LST products based on fine resolution (FR) sensors, i.e., Sentinel-2 MSI and MODIS VNIR, for estimating TSEB-based ET over South Tyrol, in the South-Eastern Alps. To this aim, we exploit relationships between CR LST and FR predictors using trees-based algorithms. Due to reduced capabilities of univariate models in complex ecosystems, multi-source predictors are considered, including multispectral reflectances, spectral indices, solar radiation, and topography. The performance of the TSEB model driven by disaggregated outputs is evaluated against original 1-km LST and ground-based fluxes from two eddy covariance towers. In general, turbulent fluxes forced with downscaled LST resulted in RMSE of 86 Wm-2 and mean bias of 55 Wm-2, which translated to 8% and 15% decrease in the respective estimates when compared to TSEB results with 1-km LST. Despite some limitations, mainly related to small-scale changes in landcover and topography that control LSTs and consequently affect TSEB-based ET estimates, the enhanced land surface temperature has potential for providing energy fluxes at finer spatial resolution in heterogenous ecosystems.

The growing production of digital content and its dissemination across the worldwide web require eficient and precise management. In this context, image quality assessment measures (IQAMs) play a pivotal role in guiding the development of numerous image processing systems for compression, enhancement, and restoration. The structural similarity index (SSIM) is one of the most common IQAMs for estimating the similarity between a pristine reference image and its corrupted variant. The multi-scale SSIM is one of its most popular variants that allows assessing image quality at multiple spatial scales. This paper proposes a two-stage genetic programming (GP) approach to evolve novel multi-scale IQAMs, that are simultaneously more effective and efficient. We use GP to perform feature selection in the first stage, while the second stage generates the final solutions. The experimental results show that the proposed approach outperforms the existing MS-SSIM. A comprehensive analysis of the feature selection indicates that, for extracting multi-scale similarities, spatially-varying convolutions are more effective than dilated convolutions. Moreover, we provide evidence that the IQAMs learned for one database can be successfully transferred to previously unseen databases. We conclude the paper by presenting a set of evolved multi-scale IQAMs and providing their interpretation.

Several contemporaneous image processing and computer vision systems rely upon the full-reference image quality assessment (IQA) measures. The single-scale structural similarity index (SS-SSIM) is one of the most popular measures, and it owes its success to the mathematical simplicity, low computational complexity, and implicit incorporation of Human Visual System's (HVS) characteristics. In this paper, we revise the original parameters of SSIM and its multi-scale counterpart (MS-SSIM) to increase their correlation with subjective evaluation. More specifically, we exploit the evolutionary computation and the swarm intelligence methods on five popular IQA databases, two of which are dedicated distance-changed databases, to determine the best combination of parameters efficiently. Simultaneously, we explore the effect of different scale selection approaches in the context of SS-SSIM. The experimental results show that with a proper fine-tuning (1) the performance of SS-SSIM and MS-SSIM can be improved, in average terms, by 8% and by 3%, respectively, (2) the SS-SSIM after the so-called standard scale selection achieves similar performance as if applying computationally more expensive state-of-the-art scale selection methods or MS-SSIM; moreover, (3) there is evidence that the parameters learned on a given database can be successfully transferred to other (previously unseen) databases; finally, (4) we propose a new set of reference parameters for SSIM's variants and provide their interpretation.

BACKGROUND: Australia persistently has one of the highest rates of colorectal cancer (CRC) in the world. Australia's National Bowel Cancer Screening Program (NBCSP) sends a biennial Faecal Immunochemical Test (FIT)-the 'NBCSP kit'-to everyone eligible for the programme between 50 and 74 years old; however, participation in the programme is low, especially in the 50- to 60-year-old age group. Our previous efficacy trial ('SMARTscreen') demonstrated an absolute increase in uptake of 16.5% (95% confidence interval = 2.02-30.9%) for people sent an SMS with motivational and instructional videos, from their general practice prior to receiving their NBCSP kit, compared to those receiving usual care. Building on the strengths of the SMARTscreen trial and addressing limitations, the 'SMARTERscreen' trial will test the effect on participation in the NBCSP of sending either an SMS only or an SMS with online video material to general practice patients due to receive their NBCSP compared to 'usual care'. METHODS: SMARTERscreen is a three-arm stratified cluster randomised controlled trial involving 63 general practices in two states in Australia. Eligible patients are patients who are aged 49-60 years and due to receive their NBCSP kit within the next 2 weeks during the intervention period. General practices will be equally randomised to three trial arms (21:21:21, estimated average 260 patients/practice). The two interventions include (i) an SMS with an encouraging message from their general practice or (ii) the same SMS with weblinks to additional motivational and instructional videos. The control arm will receive 'usual care'. Using the intention-to-treat approach, primary analysis will estimate the three pair-wise between-arm differences in the proportion of eligible patients who participate in the NBCSP within 6 months of when their kit is sent, utilising screening data from the Australian National Cancer Screening Register (NCSR). Patient intervention adherence to the inter

Background: Australia persistently has one of the highest rates of colorectal cancer (CRC) in the world. Australia’s National Bowel Cancer Screening Program (NBCSP) sends a biennial Faecal Immunochemical Test (FIT) – the ‘NBCSP kit’ - to everyone eligible for the Program between 50-74 years old, however participation in the program is low, especially in the 50- to 60-year-old age group. Our previous efficacy trial (‘SMARTscreen’) demonstrated an absolute increase in uptake of 16.5% (95% confidence interval:2.02-30.9%) for people sent an SMS with motivational and instructional videos, from their general practice prior to receiving their NBCSP kit, compared to those receiving usual care. Building on the strengths of the SMARTscreen trial and addressing limitations, the ‘SMARTERscreen’ trial will test the effect on participation in the NBCSP of sending either an SMS only or an SMS with online video material to general practice patients due to receive their NBCSP compared to ‘usual care’. Methods: SMARTERscreen is a three-arm stratified cluster randomised controlled trial involving 63 general practices in two states in Australia. Eligible patients who are aged 49-60 years and due to receive their NBCSP kit within next two weeks during the intervention period. General practices will be equally randomised to three trial arms (21:21:21, average 260 patients/practice). The two interventions include: i) an SMS with an encouraging message from their general practice, or ii) the same SMS with web-links to additional motivational and instructional videos. The control arm will receive ‘usual care’. Using the intention-to-treat approach, primary analysis will estimate the three pair-wise between-arm differences in the proportion of eligible patients who participate in the NBCSP within 6-months of when their kit is sent, utilising screening data from the Australian National Cancer Screening Register (NCSR). Patient intervention adherence to the interventions will also be

Geometric Semantic Geometric Programming (GSGP) is one of the most prominent Genetic Programming (GP) variants, thanks to its solid theoretical background, the excellent performance achieved, and the execution time significantly smaller than standard syntax-based GP. In recent years, a new mutation operator, Geometric Semantic Mutation with Local Search (GSM-LS), has been proposed to include a local search step in the mutation process based on the idea that performing a linear regression during the mutation can allow for a faster convergence to good-quality solutions. While GSM-LS helps the convergence of the evolutionary search, it is prone to overfitting. Thus, it was suggested to use GSM-LS only for a limited number of generations and, subsequently, to switch back to standard geometric semantic mutation. A more recently defined variant of GSGP (called GSGP-reg) also includes a local search step but shares similar strengths and weaknesses with GSM-LS. Here we explore multiple possibilities to limit the overfitting of GSM-LS and GSGP-reg, ranging from adaptive methods to estimate the risk of overfitting at each mutation to a simple regularized regression. The results show that the method used to limit overfitting is not that important: providing that a technique to control overfitting is used, it is possible to consistently outperform standard GSGP on both training and unseen data. The obtained results allow practitioners to better understand the role of local search in GSGP and demonstrate that simple regularization strategies are effective in controlling overfitting.

Il dibattito attorno al tema della co-programmazione e della co-progettazione è stato rilanciato dalle novità normative del nuovo codice del Terzo settore. Esso si è spesso focalizzato principalmente attorno alla sua dimensione normativa. In Italia, tuttavia, questi concetti erano già entrati stati introdotti delle politiche pubbliche locali con la legge n. 328 del 2000 e hanno generato esperienze diffuse nei territori che possono essere messe a fuoco. È così possibile ragionare sulle sfide che l’applicazione delle recenti novità normative pongono, a partire da alcuni elementi che emergono dall’esperienza dell’ultimo ventennio e così provare ad apprendere qualche lezione sull’attuazione di tali novità. L’amministrazione condivisa è dunque un’opzione che richiede alle pubbliche amministrazioni una maggior consapevolezza delle complesse dinamiche di inclusione e di ascolto implicate da un effettivo processo partecipativo e implica l’adozione di nuovi modelli di governance aperti, trasparenti ed intellegibili., The debate around co-planning and co-design has been revived by the regulatory changes of the new Third Sector Code. This debate has often focused mainly on its normative dimension. However, in Italy these concepts had already been introduced in the local public policies with the Law 328/2000 and it is possible to focus on the experiences they generated in the territories. It is possible to think about the challenges of the implementation of the recent regulatory changes, starting from some elements that emerge from the experience of the last two decades, and thus trying to learn some lessons about the implementation of the new rules. Shared administration is therefore an option that requires public administrations to be more aware of the complex dynamics of inclusion and listening implied by an effective participatory process and implies the adoption of new open, transparent and intelligible governance models.

Full-reference image quality measures are a fundamental tool to approximate the human visual system in various applications for digital data management: from retrieval to compression to detection of unauthorized uses. Inspired by both the effectiveness and the simplicity of hand-crafted Structural Similarity Index Measure (SSIM), in this work, we present a framework for the formulation of SSIM-like image quality measures through genetic programming. We explore different terminal sets, defined from the building blocks of structural similarity at different levels of abstraction, and we propose a two-stage genetic optimization that exploits hoist mutation to constrain the complexity of the solutions. Our optimized measures are selected through a cross-dataset validation procedure, which results in superior performance against different versions of structural similarity, measured as correlation with human mean opinion scores. We also demonstrate how, by tuning on specific datasets, it is possible to obtain solutions that are competitive with (or even outperform) more complex image quality measures.