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Amyotrophic lateral sclerosis (ALS)- and frontotemporal dementia (FTD)-linked mutations in CCNF have been shown to cause dysregulation to protein homeostasis. CCNF encodes for cyclin F, which is part of the cyclin F-E3 ligase complex SCFcyclinF known to ubiquitylate substrates for proteasomal degradation. In this study, we identified a function of cyclin F to regulate substrate solubility and show how cyclin F mechanistically underlies ALS and FTD disease pathogenesis. We demonstrated that ALS and FTD-associated protein sequestosome-1/p62 (p62) was a canonical substrate of cyclin F which was ubiquitylated by the SCFcyclinF complex. We found that SCFcyclin F ubiquitylated p62 at lysine(K)281, and that K281 regulated the propensity of p62 to aggregate. Further, cyclin F expression promoted the aggregation of p62 into the insoluble fraction, which corresponded to an increased number of p62 foci. Notably, ALS and FTD-linked mutant cyclin F p.S621G aberrantly ubiquitylated p62, dysregulated p62 solubility in neuronal-like cells, patient-derived fibroblasts and induced pluripotent stem cells and dysregulated p62 foci formation. Consistently, motor neurons from patient spinal cord tissue exhibited increased p62 ubiquitylation. We suggest that the p.S621G mutation impairs the functions of cyclin F to promote p62 foci formation and shift p62 into the insoluble fraction, which may be associated to aberrant mutant cyclin F-mediated ubiquitylation of p62. Given that p62 dysregulation is common across the ALS and FTD spectrum, our study provides insights into p62 regulation and demonstrates that ALS and FTD-linked cyclin F mutant p.S621G can drive p62 pathogenesis associated with ALS and FTD.

The Global Flood Awareness System (GloFAS, https://www.globalfloods.eu/) is a freely available flood forecasting service that is running fully operational as part of the Copernicus Emergency Management Service since April 2018. GloFAS offers a number of products, which are tailored to give an overview of the current and future hydro-meteorological situation. The GloFAS dataset includes medium-range and seasonal discharge forecasts, as well as storages (e.g. soil moisture, snow cover, lakes volumes) and main fluxes (e.g. surface and sub-surface runoff, actual evapotranspiration).The GloFAS dataset is generated using the open source hydrological model OS LISFLOOD (https://ec-jrc.github.io/lisflood/). OS LISFLOOD is a distributed, physically based rainfall-runoff model, which has been designed for the modelling of rainfall-runoff processes in large and transnational catchments for a variety of applications including flood simulation and forecasting; water resources assessment (drought forecast); analysis of the impacts of land use changes, river regulation measures, and other water management plans; or climate change analysis. The recent high-resolution global implementation of OS LISFLOOD allowed the delivery of the newest GloFAS set-up, namely GloFAS v4.0 which is foreseen to become operational in Q2 2023. This latest set-up has a 0.05 degrees resolution (~5km), 4 times higher than the previous version. Moreover, a crucial feature of the high-resolution implementation is the use of the latest research findings and remote sensing datasets to prepare the set of high-resolution input maps for the hydrological model. These maps allow to account more accurately for the morphological, physical, and land use characteristics of the catchments and thus enable an improved representation of the rainfall-runoff processes in different climates and socio-economic contexts at global scale.This presentation provides an overview (i) of the GloFAS v4.0 OS-LISFLOOD high-resolution implementation, (ii) of the model calibration incorporating almost 2000 gauging stations and a pragmatic regionalization approach, and (iii) of the technological solutions adopted to limit the computational time of global high-resolution simulations.OS-LISFLOOD, the high-resolution implementation maps, and GloFAS v4.0 are publicly available and they disclose opportunities for further analysis of the terrestrial water cycle fluxes and storages, and of the current and future state of global water resources.

Sustainable mobility is an increasingly significant issue that both public and private organizations consider in order to reduce emissions by their members. In this paper, the Life Cycle Assessment (LCA) approach was used to evaluate sustainable mobility. Data coming from a study carried out at the University of Foggia were processed by Gabi LCA software to estimate the environmental performance of the community members according to the methodology of the Product Environmental Footprint (PEF) guidelines 3.0. Results of the LCA were organized in different classes, creating an eco-indicator of sustainable mobility that can be applied to both the institution and individual members (called the Sustainable Mobility Indicator, SMI). The SMI, computed to assess the environmental impact of the University of Foggia, was also used to evaluate the best mobility scenario, which can be considered a benchmark. The creation of the performance classes and benchmark analysis represents an easier way to communicate sustainability based on the recommendations for achieving the sustainable development goals from the 2030 Agenda adopted by all United Nations Member States. Indeed, any organization can carry out this approach to assess its environmental impact (in terms of mobility) and shape transport policies accordingly, leading to the adoption of sustainable solutions.

Objectives Fraser syndrome is a rare congenital malformation characterized by cryptophthalmos, syndactyly and urogenital tract malformations. The association with hydrometrocolpos is infrequent, with only a few cases reported in the literature. Case presentation A 19-year-old primigravida presenting at 35 weeks of gestation, with prenatal finding of hydrometrocolpos associated with hypotelorism and microphthalmia. Pre-term cesarean delivery was performed due to breech labor and perinatal death. The autopsy confirmed hydrometrocolpos secondary to vaginal atresia and imperforate hymen, associated with cryptophthalmos, syndactyly, nasal and pinna malformations, confirming the diagnosis of Fraser syndrome. Conclusions Fraser syndrome is usually a postnatal diagnosis. The association with genital abnormalities explains the finding of hydrometrocolpos, which could be considered a diagnostic criterion for this syndrome.

Context.— Glioma is the most common primary brain tumor in adults. The diagnosis and grading of different pathological subtypes of glioma is essential in treatment planning and prognosis. Objective.— To propose a deep learning–based approach for the automated classification of glioma histopathology images. Two classification methods, the ensemble method based on 2 binary classifiers and the multiclass method using a single multiclass classifier, were implemented to classify glioma images into astrocytoma, oligodendroglioma, and glioblastoma, according to the 5th edition of the World Health Organization classification of central nervous system tumors, published in 2021. Design.— We tested 2 different deep neural network architectures (VGG19 and ResNet50) and extensively validated the proposed approach based on The Cancer Genome Atlas data set (n = 700). We also studied the effects of stain normalization and data augmentation on the glioma classification task. Results.— With the binary classifiers, our model could distinguish astrocytoma and oligodendroglioma (combined) from glioblastoma with an accuracy of 0.917 (area under the curve [AUC] = 0.976) and astrocytoma from oligodendroglioma (accuracy = 0.821, AUC score = 0.865). The multiclass method (accuracy = 0.861, AUC score = 0.961) outperformed the ensemble method (accuracy = 0.847, AUC = 0.933) with the best performance displayed by the ResNet50 architecture. Conclusions.— With the high performance of our model (>80%), the proposed method can assist pathologists and physicians to support examination and differential diagnosis of glioma histopathology images, with the aim to expedite personalized medical care.

Accurate brain tumor segmentation on magnetic resonance imaging (MRI) has wide-ranging applications such as radiosurgery planning. Advances in artificial intelligence, especially deep learning (DL), allow development of automatic segmentation that overcome the labor-intensive and operator-dependent manual segmentation. We aimed to evaluate the accuracy of the top-performing DL model from the 2018 Brain Tumor Segmentation (BraTS) challenge, the impact of missing MRI sequences, and whether a model trained on gliomas can accurately segment other brain tumor types. We trained the model using Medical Decathlon dataset, applied it to the BraTS 2019 glioma dataset, and developed additional models using individual and multimodal MRI sequences. The Dice score was calculated to assess the model’s accuracy compared to ground truth labels by neuroradiologists on BraTS dataset. The model was then applied to a local dataset of 105 brain tumors, performance of which was qualitatively evaluated. The DL model using pre- and post-gadolinium contrast T1 and T2 FLAIR sequences performed best, with a Dice score 0.878 for whole tumor, 0.732 tumor core, and 0.699 active tumor. Lack of T1 or T2 sequences did not significantly degrade performance, but FLAIR and T1C were important contributors. All segmentations performed by the model in the local dataset, including non-glioma cases, were considered accurate by a pool of specialists. The DL model could use available MRI sequences to optimize glioma segmentation and adopt transfer learning to segment non-glioma tumors, thereby serving as a useful tool to improve treatment planning and personalized surveillance of patients.

The Italian export of agri-food products has been increasingly threatened by the unfair use of misleading Italian symbols (such as the national flag or the green-white-red colors) by non-Italian producers. This research paper investigated what English and Spanish consumers know about “Made in Italy” food, and their attitude towards Italian appearance food products. Primary data were collected in Spain and England, and a probit model was used to identify the determinants of consumers’ vulnerability to misleading Italian symbols. We found that merely having Italian symbols on the package might lead almost half of the consumers in the sample to consider food as Made in Italy, regardless of the actual origin. This result confirms the severity of the problem. The econometric model provides suggestions for public actions to mitigate the issue.

Purpose The purpose of this paper is to evaluate the market potential of food obtained from olive by-products. The marketing of such by-products (e.g. olive leaves and pulp) is a challenging opportunity for the sustainable development of the sector. Yet, consumer demand is still poorly understood. The paper contributes to filling the knowledge gap with an empirical survey of a sample of Italian consumers. Design/methodology/approach The authors provide an assessment of consumers’ willingness to accept (WTA) food from olive by-products. The authors collected structured questionnaire from a sample of 289 Italian consumers. The authors asked to consumers their willingness to try a variety of food products containing olive by-products, as a proxy for their WTA the products. In order to investigate the drivers of the average WTA, the authors used the information in the questionnaire to build four constructs of interest: technophobia, neophobia, perception of benefits and awareness about sustainable consumption. The choice of the constructs and the variables was driven by the existing literature. Findings The paper shows how the WTA food with olive by-products is a general attitude of the consumer, rather than product-specific choice. The results suggest that consumers perceive the use of olive by-products as a new technology for preparing well-known food products. The authors did not find statistical evidence of the wariness of olive by-products as new food products. Technophobia is the most important factor hampering the marketing of olive by-products. Originality/value The paper is a first attempt of exploring the topic of WTA food with olive by-products.

The heterogeneity of brain tumours at the molecular, metabolic and structural levels poses significant challenge for accurate tissue characterisation. Artificial intelligence and radiomics have emerged as valuable tools to analyse quantitative features extracted from medical images which capture the complex microenvironment of brain tumours. In particular, a number of computational tools including machine learning algorithms have been proposed for image preprocessing, tumour segmentation, feature extraction, classification, and prognostic stratifications as well. In this chapter, we explore the fundamentals of multiparametric brain tumour characterisation, as an understanding of the strengths, limitations and applications of these tools allows clinicians to better develop and evaluate models with improved diagnostic and prognostic value in brain tumour patients.

The heterogeneity of brain tumours at the molecular, metabolic and structural levels poses significant challenge for accurate tissue characterisation. Artificial intelligence and radiomics have emerged as valuable tools to analyse quantitative features extracted from medical images which capture the complex microenvironment of brain tumours. In particular, a number of computational tools including machine learning algorithms have been proposed for image preprocessing, tumour segmentation, feature extraction, classification, and prognostic stratifications as well. In this chapter, we explore the fundamentals of multiparametric brain tumour characterisation, as an understanding of the strengths, limitations and applications of these tools allows clinicians to better develop and evaluate models with improved diagnostic and prognostic value in brain tumour patients.

Thanks to the development of increasingly sophisticated machine-learning techniques, it is possible to improve predictions of a certain phenomenon. In this paper, after having analyzed data relating to the mobility habits of University of Foggia (UniFG) community members and deter- mined their emissions of pollutants, we applied machine-learning techniques to these data to estimate the quantities of pollutants (in a certain time period) produced by new subjects not present in the data sets, using very little information. In this way, we developed a method that the university could apply to inform new students about what their emissions of pollutants could be in the near future, through several easily obtainable features. This method could allow the UniFG Rectorate to improve its sustainable mobility policies by encouraging the use of methods that are as appropriate as possible to the users’ needs. In addition, any public/private organization outside the academic environment can use the method, due to the need for little information.

Purpose To train deep learning convolutional neural network (CNN) models for classification of clinically significant Chiari malformation type I (CM1) on MRI to assist clinicians in diagnosis and decision making. Methods A retrospective MRI dataset of patients diagnosed with CM1 and healthy individuals with normal brain MRIs from the period January 2010 to May 2020 was used to train ResNet50 and VGG19 CNN models to automatically classify images as CM1 or normal. A total of 101 patients diagnosed with CM1 requiring surgery and 111 patients with normal brain MRIs were included (median age 30 with an interquartile range of 23–43; 81 women with CM1). Isotropic volume transformation, image cropping, skull stripping, and data augmentation were employed to optimize model accuracy. K-fold cross validation was used to calculate sensitivity, specificity, and the area under receiver operating characteristic curve (AUC) for model evaluation. Results The VGG19 model with data augmentation achieved a sensitivity of 97.1% and a specificity of 97.4% with an AUC of 0.99. The ResNet50 model achieved a sensitivity of 94.0% and a specificity of 94.4% with an AUC of 0.98. Conclusions VGG19 and ResNet50 CNN models can be trained to automatically detect clinically significant CM1 on MRI with a high sensitivity and specificity. These models have the potential to be developed into clinical support tools in diagnosing CM1.

Carbon (C) budget at cropping systems has not only agronomic but also environmental relevance because of their contribution to both emissions and removals of greenhouse gases (GHGs). Ideally, sustainable orchards are expected to remove atmospheric CO2 at a rate greater than that of the emissions because of (i) optimized biology of the system and (ii) reduced on-site/offsite inputs sourced by the technosphere. However, such a computation might produce inconsistent results and in turn biased communication on sustainability of the cropping systems because C accounting framework(s) are used under unclear context. This study examined the sustainability of orchards in terms of impact on GHGs focusing its significance at the field, ecosystem and global dimension analyzing some operational aspects and limitations of existing frameworks (e.g., net ecosystem carbon balance (NECB), life cycle assessment (LCA)). Global relevance of sustainable orchard was also discussed considering the C sequestration at cropland as instructed by Intergovernmental Panel on Climate Change (IPCC). The uniqueness of olive tree lifespan duration and C sequestration is discussed within the Product Environmental Footprint of agrifood product. The paper also highlighted overlapping components among the NECB, LCA and IPCC frameworks and the need for an integrated C accounting scheme for a more comprehensive and detailed mapping of sustainability in agriculture.

Outlier scanpaths identification is a crucial preliminary step in designing visual software, digital media analysis, radiology training and clustering participants in eye-tracking experiments. However, the task is challenging due to the visual irregularity of the scanpath shapes and the difficulty in dimensionality reduction due to geometric complexity. Conventional approaches have used heat maps to exclude scanpaths that lack a similarity pattern. However, the typically-used packages, such as ScanMatch and MultiMatch often generate discordant results when outlier identification is done empirically. This paper introduces a novel outlier evaluation approach by integrating the fractal dimension (FD), capturing the geometrical complexity of patterns, as an additional parameter with the heat map. This additional parameter is used to evaluate the degree of influence of a scanpath within a dataset. More specifically, the 2D Cartesian coordinates of a scanpath are fitted to a space filling 1D fractal curve to characterise its temporal FD. The FDs of the scanpaths are then compared to match their geometric complexity to one another. The findings indicate that the FD can be a beneficial additional parameter when evaluating the candidacy of poorly matching scanpaths as outliers and performs better at identifying unusual scanpaths than using other methods, including scanpath matching, Jaccard, or bounding box methods alone., Higuchi fractal dimension; Visual scanpath; Hilbert curve; Outlier; Computational neuroscience

Recent studies matching eye gaze patterns with those of others contain research that is heavily reliant on string editing methods borrowed from early work in bioinformatics. Previous studies have shown string editing methods to be susceptible to false negative results when matching mutated genes or unordered regions of interest in scanpaths. Even as new methods have emerged for matching amino acids using novel combinatorial techniques, scanpath matching is still limited by a traditional collinear approach. This approach reduces the ability to discriminate between free viewing scanpaths of two people looking at the same stimulus due to the heavy weight placed on linearity. To overcome this limitation, we here introduce a new method called SoftMatch to compare pairs of scanpaths. SoftMatch diverges from traditional scanpath matching in two different ways: firstly, by preserving locality using fractal curves to reduce dimensionality from 2D Cartesian (x,y) coordinates into 1D (h) Hilbert distances, and secondly by taking a combinatorial approach to fixation matching using discrete Fréchet distance measurements between segments of scanpath fixation sequences. These matching “sequences of fixations over time” are a loose acronym for SoftMatch. Results indicate high degrees of statistical and substantive significance when scoring matches between scanpaths made during free-form viewing of unfamiliar stimuli. Applications of this method can be used to better understand bottom up perceptual processes extending to scanpath outlier detection, expertise analysis, pathological screening, and salience prediction.

We developed an autologous hematopoietic stem cell-based platform designed to deliver IFNa, by a transcriptional and post-transcriptional control mechanism mediated by miRNA target sequences, specifically into the tumor microenvironment (TME) via Tie-2 expressing monocytes (Temferon). As of Feb 2022, 3 escalating doses of Temferon (0.5-2.0x106/kg) were tested across 15 newly diagnosed, unmethylated MGMT GBM patients assigned to 5 cohorts. Follow-up from surgery is 6–28mo (2–25mo after Temferon). To date, no DLTs have been identified. As expected, 1mo after the administration of the highest tested dose, the hematopoietic system of Temferon-treated patients was composed of up to 30% of CD14+ modified cells. Temferon-derived progeny persisted, albeit at lower levels, up to 18mo (longest time of analysis). Despite the substantial proportion of engineered cells, very low concentrations of IFNα were detected in the plasma and in the CSF, indicating tight regulation of transgene expression. SAEs were mostly attributed to conditioning chemotherapy (infections) or disease progression (seizures). 1SUSAR (persistent GGT elevation) occurred. Median OS is 15mo from surgery. Homing of transduced cells to the tumor was demonstrated by the presence of gene-marked cells in the 2nd surgery specimens of 3 out 4 pts belonging to low dose cohorts. Single-cell RNA seq of the TME highlighted a Temferon signature associated with the induction IFNa responsive genes and macrophage repolarization. Potential long-term benefit with Temferon was identified in a patient from cohort 3, who had PD at D+120 with two distant enhancing lesions, and increased tumor necrosis. 1y following Temferon, with no 2nd-line therapy added, there was approximately 40% reduction in enhancing tumor volume compared to D+180 with a stable clinical and imaging picture thereafter. The results provide initial evidence of Temferon’s potential to modulate the TME of GBM patients, and anecdotal evidence for long lasting effects of Temferon in prevention of disease progression.

Increasing clinical use of immune checkpoint inhibitors testifies to the importance of modulating the immune TME to obtain meaningful anti-tumor immune responses. Acting only on T lymphocytes may, however, not be sufficient, e.g. in immunologically-cold tumors or due to de novo or acquired resistance. Moreover, immune-related AEs remain hurdles of T cell therapies. To overcome these limitations and to awaken the immune system in an agnostic way against the tumor, we have developed a genetically modified cell-based autologous hematopoietic stem cell platform (Temferon) delivering immunotherapeutic payloads into the TME through Tie-2 expressing monocytes (TEMs), a subset of tumor infiltrating macrophages. TEM-GBM is an ongoing open-label, Phase 1/2a dose-escalating study evaluating the safety & efficacy of Temferon in up to 21 newly diagnosed patients with glioblastoma & unmethylated MGMT promoter assigned to 7 different cohorts (3 pts each) differing by Temferon dose (0.5-4.0x106/kg) and conditioning regimen (BCNU+ or Busulfan+Thiotepa). By Oct 15th, 2021, 15 pts (cohort 1-5) had received escalating doses of Temferon with a median follow up of 267 days (range: 60-749). Rapid engraftment and hematological recovery from nonmyeloablative conditioning occurred in all pts. Temferon-derived differentiated cells, as determined by the presence of vector genomes in the DNA, were found at increasing proportions in PB and BM, reaching up to 30% at 1 month for the highest cohorts tested (2.0x106/kg) and persisting up to 18 months, albeit at lower levels. Despite the significant proportion of engineered cells, only very low median concentrations of IFNα were detected in the plasma (D+30, 5.9; D+90, 8.8pg/mL) and in the cerebrospinal fluid (D+30, 1.5; D+90, 2.4pg/mL), indicating tight regulation of vector expression. SAEs were mostly attributed to conditioning chemotherapy (e.g. infections) or disease progression (e.g. seizures). 1 SUSAR (persistent GGT elevation) has occurred. Median OS is 14 mth from surgery (11 mth post Temferon). Four pts from the low dose cohorts underwent 2nd surgery. These recurrent tumors contained gene-marked cells and expressed IFN-responsive genes, indicative of local IFNα release by TEMs. In 1 pt, a stable lesion (as defined by MRI) had a higher proportion of T cells & TEMs, an increased IFN-response signature and myeloid re-programming revealed by scRNAseq, as compared to a synchronous, progressing tumor. TCR sequencing of blood and tumor samples showed a post-treatment increase in the cumulative frequency of tumor-associated T cell clones identified in 1st and 2nd surgery specimens (up to 4 out of 9 subjects). These results provide initial evidence for on-target activity of Temferon in GBM, to be consolidated with longer follow up in the higher dose cohorts. Citation Format: Bernhard Gentner, Gaetano Finocchiaro, Francesca Farina, Marica Eoli, Alessia Capotondo, Elena Anghileri, Matteo Barcella, Maria Grazia Bruzzone, Matteo Giovanni Carrabba, Valeria Cuccarini, Giorgio D'Alessandris, Francesco Di Meco, Valeria Ferla, Paolo Ferroli, Filippo Gagliardi, Federico Legnani, Pietro Mortini, Matteo Maria Naldini, Alessandro Olivi, Roberto Pallini, Monica Patanè, Rosina Paterra, Bianca Pollo, Marco Saini, Silvia Snider, Valentina Brambilla, Stefania Mazzoleni, Andrew Zambanini, Carlo Russo, Luigi Naldini, Fabio Ciceri. Genetically modified Tie-2 expressing monocytes target IFN-α2 to the glioblastoma tumor microenvironment (TME): Preliminary data from the TEM-GBM Phase 1/2a study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5213.

2040 Background: Immunotherapies represent powerful tools that are transforming the treatment of many cancers. However, immune dysfunction in cancer is multifactorial requiring multiple points of action, especially in immunologically-cold tumors. Methods: We have developed a genetically modified, autologous hematopoietic stem cell-based platform designed to deliver Interferon-alpha (IFNa) specifically into the tumor microenvironment through Tie-2 expressing monocytes (Temferon), in order to activate the immune system in an agnostic way against the tumor and re-establish immunosurveillance. Results: As of Jan 2022, 3 escalating doses of Temferon (from 0.5 to 2.0x106/kg) were tested across 15 patients assigned to 5 cohorts affected by newly diagnosed, unmethylated MGMT glioblastoma (GBM). The follow-up range from surgery is 5 – 27 mo (3 – 24 mo after Temferon). In all patients, we observed rapid engraftment of gene modified progenitors and fast recovery from sub-myeloablative conditioning (median engraftment across all the cohorts: Neu D+13, PLT D+14). Temferon-derived differentiated cells, as determined by the presence of vector genomes in the DNA, were found at increasing proportions in blood and bone marrow, reaching up to 30% at 1 mo for the highest dose cohorts tested and persisting up to 18 mo, albeit at lower levels. Despite the significant proportion of engineered cells, only very low median concentrations of IFNα were detected in the plasma (D+30, 5.9; D+90, 8.8pg/mL) and in the CSF (D+30, 1.5; D+90, 2.4pg/mL), indicating tight regulation of vector expression. SAEs were mostly attributed to conditioning chemotherapy (e.g. infections) or disease progression (e.g. seizures). 1 SUSAR (persistent GGT elevation) has occurred. Median OS is 14 mo from surgery (10 mo post Temferon). A patient from cohort 3, had at D+120 disease progression with two distant enhancing lesions, and increased tumor necrosis. One year following Temferon, with no 2nd line therapy added, there was approximately 40% reduction in enhancing tumor volume compared to D+180. Four pts from the low dose cohorts underwent 2nd surgery. Vector genomes were detectable in tumor biopsies. Single cell RNA seq performed on CD45+ cells purified from the GBM TME highlighted the presence of an Interferon gene signature in all patients, resulting in macrophage repolarization in some of them. Conclusions: Our interim results show that Temferon is well tolerated, with no dose limiting toxicities identified to date. The results provide initial evidence of Temferon’s potential to modulate the TME of GBM patients. Clinical trial information: NCT03866109.

Imperfect competition in the food supply chain is a concern for several “weak agents” such as small farmers and consumers. Given the difficulties in applying standard anti-trust regulations in the agri-food system, a growing interest is emerging in a decentralized approach where private entities, such as Producer Organizations (PO), are given active role in the governance of the agricultural markets. We present a simple bargaining model assessing the POs’ capacity to rebalance bargaining power along the supply chain. The results show that POs can benefit farmers by strengthening their negotiation power, improving their bargaining position and worsening the buyer’s one. Imposing a minimum size for POs may improve their effectiveness in affecting the buyer’s bargaining position. Such requirement should be calibrated on the structure of the downstream (upstream) relevant market.

To determine the heterogeneity of glucose uptake applying fractal analysis on positron emission tomography/computed tomography (PET/CT) with 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) images in patients with non-small cell lung carcinoma (NSCLC) before surgery, and to assess whether this heterogeneity was associated with disease-free survival (DFS). [18F]FDG PET/CT scans of 113 patients’ prior surgery were retrospectively revised. PET DICOM images were analyzed for fractal geometry using a ad hoc software to automatically determine the following indexes: (a) mean intensity value (MIV), (b) standard deviation (SD), (c) relative dispersion (RD), (d) three-dimensional (3D) histogram of the fractal dimension (3D HIST FR DIM), and (e) fractal dimension in 3D (3D-FD). All the fractal indexes were subsequently compared with metabolic parameters and disease-free survival (DFS). We found a significant correlation between 3D-FD and SUVmax, SUVmean, MTV, and TLG. Additionally, positive correlations between MIV, SD, and all metabolic parameters were also detected. Patients with high 3D-FD tumor (≥ 1.62) showed significantly higher values of SUVmax, SUVmean, MTV, and TLG than those with lower 3D-FD. In univariate analysis, median 3D-FD and median TLG were significantly associated with DFS (p = 0.04 and p = 0.03, respectively). These findings were confirmed on log-rank test. On multivariate analysis, among age, stage disease, histotype, 3D-FD, and metabolic parameters, only 3D-FD was identified as independent prognostic factor for DFS (p = 0.032; HR 0.418, 95 % CI 0.189–0.926). 3D-FD was different between adenocarcinoma and squamous cell carcinoma (1.60 versus 1.88, p = 0.014), and 3D-FD value was found higher in advanced stage disease. Metabolic heterogeneity determined applying fractal principles on PET images can be considered as a novel imaging biomarker for survival in patients with NSCLC.

In this paper, we investigate the short-term and long-term effects of the COVID-19 emergency on consumers' decision of changing dietary habit. We used a certified dataset reporting information about 456 Italian consumers during the lockdown in the first wave of the pandemic emergency (April 2020). The survey collected data about changes in food purchases, respondents' mood during the lockdown, conspiracist beliefs, exposure to the virus, and planned food purchasing behavior after the lockdown. We used the data to construct measures of the psychological pressure exerted by the COVID-19 emergency on consumers. We use an endogenous selection regression model to assess the impact of psychological pressure on the decision of changing food purchased. The analysis identified two opposite approaches to change in food purchasing decisions: impulsive approach and reflective approach. The former is associated with a higher probability of changing food purchase but a lower probability to keep the changes in the long run than the latter. Our results suggest that COVID-19 psychological pressure was associated with impulsive approach to buy food. Consequently, food-purchasing behavior is expected to revert to pre-COVID 19 habits when the emergency is over., COVID-19 emergency; Conspiracist beliefs; Food choices; Impulsive buying, Reflective buying, Eating behaviour