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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.

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.

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.

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.

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.

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

Pre-processing and Data Augmentation play an important role in Deep Convolutional Neural Networks (DCNN). Whereby several methods aim for standardization and augmentation of the dataset, we here propose a novel method aimed to feed DCNN with spherical space transformed input data that could better facilitate feature learning compared to standard Cartesian space images and volumes. In this work, the spherical coordinates transformation has been applied as a preprocessing method that, used in conjunction with normal MRI volumes, improves the accuracy of brain tumor segmentation and patient overall survival (OS) prediction on Brain Tumor Segmentation (BraTS) Challenge 2020 dataset. The LesionEncoder framework has been then applied to automatically extract features from DCNN models, achieving 0.586 accuracy of OS prediction on the validation data set, which is one of the best results according to BraTS 2020 leaderboard.

GBM with an unmethylated MGMT gene promoter is associated with very poor prognosis. A subset of tumor associated macrophages expressing the angiopoietin receptor Tie2 (TEMs) can be genetically modified for local & tumor restricted release of interferon-α2 (IFN). IFN has antitumor effects, inhibits angiogenesis & modulates the immune system. Temferon consists of autologous HSPCs transduced ex-vivo with an LVV encoding an IFN gene & expression control sequences for TEMs. TEM-GBM is an open-label, Phase I/IIa study (Part A: 3x3x3 dose escalation; Part B: n=12), & Temferon (single dose) is given to patients with first diagnosis of GBM & unmethylated MGMT promoter. Part A 3rd cohort is ongoing & completes dosing in September 2020. Eight patients completed screening; one patient died (disease progression) before Temferon was administered. Six patients received Temferon (3 women, 3 men, mean age 52.3 years). Cohort 1 received Temferon 0.5x106 cells/kg & Cohort 2, 1x106 cells/kg. Neutropenia & thrombocytopenia occurred as expected following conditioning & hematologic recovery (HR) occurred median D+13. Transduced PBMCs were identified by vector copy number (VCN) on myeloid cells at HR & at later timepoints. In general, a dose-ordered increase in VCN was observed (mean VCN D+30 CD14+ Cohort 1: 0.094, cohort 2: 0.125); 1 patient in each cohort had low VCNs. VCN remained detectable up to recent follow up visits (≤ D+180). No dose-limiting toxicities have been reported. Four SAEs occurred in 3 patients who received Temferon (pneumonia, pulmonary embolism, febrile neutropenia, fatigue) but these events were not attributed to Temferon, resolved, & may have been related to the conditioning regimen (carmustine & thiotepa). Disease progression has been confirmed in 3 patients who received Temferon. These preliminary results indicate feasibility of engrafting a pre-determined fraction of Temferon cells in the bone marrow of GBM patients without, so far, causing dose-limiting toxicity.

In this paper, we investigated educated millennials&rsquo, evaluation of credence attributes in food products containing genetically modified organisms (GMO products). Our goal is to assess whether beliefs about GMO products are determined by scientific knowledge alone or if they are affected by other factors such as trust in information providers and use of social media. The focus on millennials is motivated by the increasing relevance of this social group in the public debate and by their extensive use of social media. We surveyed a sample of 215 Italian college students, confronting them with questions about safety, environmental impact and ethical issues in GMO product consumption. Using an ordered probit regression model, we found that educated millennials build their beliefs using a mix of scientific knowledge and trust in information providers. The role of the two drivers depended on the issue considered. Scientific knowledge drove beliefs in health claims, while trust in information providers was a driving factor in almost all claims. After controlling for trust effects, we did not find evidence of impact of confidence in the reliability of traditional and social media on beliefs. This result contradicts previous literature.

Mutations in isocitrate dehydrogenase genes IDH1 and IDH2 are frequently found in diffuse and anaplastic astrocytic and oligodendroglial tumours as well as in secondary glioblastomas. As IDH is a very important prognostic, diagnostic and therapeutic biomarker for glioma, it is of paramount importance to determine its mutational status. The haematoxylin and eosin (H&E) staining is a valuable tool in precision oncology as it guides histopathology-based diagnosis and proceeding patient’s treatment. However, H&E staining alone does not determine the IDH mutational status of a tumour. Deep learning methods applied to MRI data have been demonstrated to be a useful tool in IDH status prediction, however the effectiveness of deep learning on H&E slides in the clinical setting has not been investigated so far. Furthermore, the performance of deep learning methods in medical imaging has been practically limited by small sample sizes currently available. Here we propose a data augmentation method based on the Generative Adversarial Networks (GAN) deep learning methodology, to improve the prediction performance of IDH mutational status using H&E slides. The H&E slides were acquired from 266 grade II-IV glioma patients from a mixture of public and private databases, including 130 IDH-wildtype and 136 IDH-mutant patients. A baseline deep learning model without data augmentation achieved an accuracy of 0.794 (AUC = 0.920). With GAN-based data augmentation, the accuracy of the IDH mutational status prediction was improved to 0.853 (AUC = 0.927) when the 3,000 GAN generated training samples were added to the original training set (24,000 samples). By integrating also patients’ age into the model, the accuracy improved further to 0.882 (AUC = 0.931). Our findings show that deep learning methodology, enhanced by GAN data augmentation, can support physicians in gliomas’ IDH status prediction.

Susceptibility-weighted imaging (SWI) is a technique useful for evaluation of the internal structures of brain tumors, including microvasculature and microbleeds. Intratumoral patterns of magnetic susceptibility can be quantified by means of fractal analysis. Here, we propose a radiomics methodological pipeline to merge advanced fractal-based computational modelling with statistical analysis to objectively characterize the fingerprint of gliomas and brain metastases. Forty-seven patients with glioma (grades II-IV, according to the WHO 2016 classification system) and fourteen with brain metastases underwent 3 Tesla MRI using a SWI protocol. All images underwent computational analysis aimed to quantify three Euclidean parameters (related to tumor and SWI volume) and five fractal-based parameters (related to the pixel distribution and geometrical complexity of the SWI patterns). Principal components analysis, linear and quadratic discriminant analysis, K-nearest neighbor and support vector machine methods were used to discriminate between tumor types. The combination of parameters offered an objective evaluation of the SWI pattern in gliomas and brain metastases. The model accurately predicted 88% of glioblastoma, according to the quantification of intratumoral SWI features, failing to discriminate the other types. SWI is not normally used to classify brain tumors, however fractal-based multi-parametric computational analysis can be used to characterize intratumoral SWI patterns to objectively quantify tumors-related features. Specific parameters still have to be identified to provide completely automatic computerized differential diagnosis.

Magnetic Resonance Imaging (MRI) is used in everyday clinical practice to assess brain tumors. Several automatic or semi-automatic segmentation algorithms have been introduced to segment brain tumors and achieve an expert-like accuracy. Deep Convolutional Neural Networks (DCNN) have recently shown very promising results, however, DCNN models are still far from achieving clinically meaningful results mainly because of the lack of generalization of the models. DCNN models need large annotated datasets to achieve good performance. Models are often optimized on the domain dataset on which they have been trained, and then fail the task when the same model is applied to different datasets from different institutions. One of the reasons is due to the lack of data standardization to adjust for different models and MR machines. In this work, a 3D Spherical coordinates transform during the pre-processing phase has been hypothesized to improve DCNN models' accuracy and to allow more generalizable results even when the model is trained on small and heterogeneous datasets and translated into different domains. Indeed, the spherical coordinate system avoids several standardization issues since it works independently of resolution and imaging settings. Both Cartesian and spherical volumes were evaluated in two DCNN models with the same network structure using the BraTS 2019 dataset. The model trained on spherical transform pre-processed inputs resulted in superior performance over the Cartesian-input trained model on predicting gliomas' segmentation on tumor core and enhancing tumor classes (increase of 0.011 and 0.014 respectively on the validation dataset), achieving a further improvement in accuracy by merging the two models together. Furthermore, the spherical transform is not resolution-dependent and achieve same results on different input resolution., Comment: 26 pages, 5 figures. Submitted to Computer Methods and Programs in Biomedicine

The manufacturing trend nowadays seems to be in contrast with the ecosystem needs, as it is based on the exploitation of limited natural resources. A new paradigm is now driving manufacturing in the direction of sustainability: the green and circular thinking. This latter can now guide SMEs in optimizing its processes and products to be more eco-friendly, and this in turn may lead to significant competitive advantages. The purpose of the study is to analyze the environmental lifecycle performance of a 100% PET fabric used for the outer cover of bed mattresses made by an Italian company, with looms of recent technology (2009–2010). The emissions defined in the community labeling system for textile products and mattresses have been taken into account as well as the mid and final impact categories established by the guidelines for the Product Environmental Footprint developed by European. Results show that the fiber and yarn production processes are the most impacted due to transport from China and the use of Chinese electricity grid mix (rich in coal). Furthermore, the disposal of fabric is another environmental critical point. In order to evaluate the improvements in the environmental sustainability of supply of PET yarns and the disposal phase, a number of scenarios were analysed, while a Monte Carlo simulation was performed to evaluate the sensitivity of the results respect to data uncertainty. A number of recommendations for the textile sector are provided, useful to assure sustainability of production. Critical points to achieve environmental labeling are highlighted and new data about textile, not already available in databases and literature, are provided due to the company's engagement. Finally, hints on the best recycling process of the product are given.

Introduction Eye-tracking research has been widely used in radiology applications. Prior studies exclusively analysed either temporal or spatial eye-tracking features, both of which alone do not completely characterise the spatiotemporal dynamics of radiologists’ gaze features. Purpose Our research aims to quantify human visual search dynamics in both domains during brain stimuli screening to explore the relationship between reader characteristics and stimuli complexity. The methodology can be used to discover strategies to aid trainee radiologists in identifying pathology, and to select regions of interest for machine vision applications. Method The study was performed using eye-tracking data 5 seconds in duration from 57 readers (15 Brain-experts, 11 Other-experts, 5 Registrars and 26 Naïves) for 40 neuroradiological images as stimuli (i.e., 20 normal and 20 pathological brain MRIs). The visual scanning patterns were analysed by calculating the fractal dimension (FD) and Hurst exponent (HE) using re-scaled range (R/S) and detrended fluctuation analysis (DFA) methods. The FD was used to measure the spatial geometrical complexity of the gaze patterns, and the HE analysis was used to measure participants’ focusing skill. The focusing skill is referred to persistence/anti-persistence of the participants’ gaze on the stimulus over time. Pathological and normal stimuli were analysed separately both at the “First Second” and full “Five Seconds” viewing duration. Results All experts were more focused and a had higher visual search complexity compared to Registrars and Naïves. This was seen in both the pathological and normal stimuli in the first and five second analyses. The Brain-experts subgroup was shown to achieve better focusing skill than Other-experts due to their domain specific expertise. Indeed, the FDs found when viewing pathological stimuli were higher than those in normal ones. Viewing normal stimuli resulted in an increase of FD found in five second data, unlike pathological stimuli, which did not change. In contrast to the FDs, the scanpath HEs of pathological and normal stimuli were similar. However, participants’ gaze was more focused for “Five Seconds” than “First Second” data. Conclusions The HE analysis of the scanpaths belonging to all experts showed that they have greater focus than Registrars and Naïves. This may be related to their higher visual search complexity than non-experts due to their training and expertise.

Background: We developed a macrophage-based treatment relying on ex vivo transduction of autologous hematopoietic stem and progenitor cells (HSPC) to express immune-payloads within the TME. Our ATMP (Temferon) targets IFN-a, an immune-modulatory molecule counteracting also neo-angiogenesis and tumor growth, to a subset of Tie2-expressing, tumor-infiltrating macrophages known as TEMs. Materials and Methods: TEM-GBM is an open-label, Phase I/IIa dose-escalation study evaluating safety and efficacy of Temferon in up to 21 newly diagnosed glioblastoma patients with unmethylated MGMT promoter. Key eligibility criteria include age 18-70 years, ECOG 0-1 and KPS >70%, and adequate cardiac, renal, hepatic and pulmonary function. Important exclusion criteria include the presence of active autoimmune disease or receipt of any oral or parenteral chemotherapy or immunotherapy within 2 years of screening. Autologous CD34+ HSPC are mobilized with lenograstim and plerixafor, collected by apheresis, purified and transduced ex vivo with a 3 rd generation lentiviral vector encoding for IFN-a2. Transgene expression is confined to TEMs by the Tie2 promoter and post-transcriptional regulation by microRNA-126 thus achieving tumor specificity. The study evaluates safety and biological activity of Temferon in 7 cohorts of three patients each, where escalating doses of Temferon are co-administered with a fixed CD34+ cell dose of non-manipulated supporter cells following a sub-myeloablative conditioning regimen (Thiotepa + BCNU or + Busulfan). The primary endpoints for this study are: Engraftment of Temferon over the first 90 DaysThe proportion of patients achieving hematologic recovery by Day +30 from ASCTShort-term tolerability of Temferon; stable blood counts and absence of cytopenias, absence of significant organ toxicities (> grade 2); absence of Replication Competent Lentivirus The figure below reports the TEM-GBM study design. Results: As of 28th June 2021, 18 patients have been enrolled; 15 received Temferon (D+0) with follow-up of 30 - 697 days. There was rapid engraftment and hematological recovery after the conditioning regimen. Median neutrophil and platelet engraftment occurred at D+13 and D+12 for patients in cohort 1-3 and D+16 and D+15 for patients assigned to cohort 4 and 5, respectively. Temferon-derived differentiated cells, as determined by the presence of vector genomes in the DNA of peripheral blood and bone marrow cells, were found within 14 days post treatment and persisted subsequently, albeit at lower levels (up to 18 months). Very low concentrations of IFNa were detected in the plasma (average 7.8 pg/ml at D+30; baseline < LLOQ) and in the cerebrospinal fluid (average 1.6 pg/ml at D+30; baseline < LLOQ), suggesting tight regulation of transgene expression. Seven deaths occurred: six at D+241, +322, +340, +402, +478, +646 after Temferon administration due to disease progression, and one at D+60 due to complications following the conditioning regimen. Nine patients had progressive disease (PD; range D-12 to +239). SAEs include infections, venous thromboembolism, brain abscess, hemiparesis, GGT elevation and poor performance status compatible with autologous stem cell transplantation, concomitant medications and PD. Four patients underwent second surgery. These recurrent tumors had gene-marked cells present and increased expression of IFN-responsive gene signatures compared to diagnosis, indicative of local IFNa release by TEMs. In one patient, a stable lesion (as defined by MRI) had a higher proportion of T cells and TEMs within the myeloid infiltrate and an increased IFN-response signature than in a progressing lesion. The T-cell immune repertoire changed with evidence for expansion of tumor-associated clones. Tumor microenvironment characterization by scRNA and TCR sequencing is ongoing. Conclusion: These interim results show that Temferon is generally well tolerated by patients, with no dose limiting toxicities identified to date. The results provide initial evidence of Temferon's potential to activate the immune system and reprogram the tumor microenvironment (TME), as predicted by preclinical studies. Figure 1 Figure 1. Disclosures Naldini: Genenta Science: Consultancy, Current equity holder in publicly-traded company, Membership on an entity's Board of Directors or advisory committees, Other: Founder.

Computational Neurosurgery is a novel field where computational modeling and artificial intelligence (AI) are used to analyze diseases of neurosurgical interest. Our aim is to apply AI models to brain tumor (BT) images to a) automatically segment BTs on pre-operative MRI, b) predict the genetic subtype of glioma on intra- and post-operative histological specimens; and c) predict the extent of resection according to connectomics data. For the segmentation task, we used 510 BT images to train a deep learning (DL) model for automatic segmentation of the tumors’ edges and comparison of the AI-generated masks versus experts’ consensus (quantified by means of the dice score). For the histopathology task, we digitalized 266 hematoxylin/eosin slides of gliomas (including 130 IDH-wildtype and 136 IDH-mutant) and applied a DL architecture to predict the IDH genetic status, then validated by immunohistochemistry and genetic sequencing. The datasets were also augmented by generating synthetic glioma images by means of a Generative Adversarial Network methodology. The resection of 10 BTs was also customized according to connectomics data. In the segmentation experiment, we reached a dice score of ~0.9 (out of 1.0), while further demonstrating that only the T1, T1 after gadolinium, and FLAIR sequences are necessary for accurate automatic segmentation. In the histopathology task, we were able to predict the genetic status with accuracy between 76% and 95% using the DL model. The machine learning-based connectome analysis allowed us to perform safe supramaximal resection. We have shown the robustness of applying AI methodology or the automatic segmentation of BTs in MR imaging. Moreover, we have also shown that AI can be used to predict the genetic status, specifically, IDH, in histopathology images of gliomas. Our results support the use of AI in the clinical scenario for a fast and objective computerized characterization of patients affected by BTs.

Temferon is an ex vivo gene therapy consisting of autologous HSPCs genetically modified to deliver IFN-α2 within the tumor microenvironment (TME) by Tie-2 expressing macrophages. TEM-GBM is an open-label, Phase I/IIa dose-escalation study evaluating safety and efficacy of Temferon in up to 21 newly diagnosed GBM patients with unmethylated MGMT. Autologous HSPCs are transduced with a LVV encoding for IFN-a2 gene. As of 30th April 2021, 18 patients have been enrolled; 13 received Temferon (D+0) with follow-up of 8 – 662 days. After conditioning and Temferon infusion, a rapid engraftment and hematological recovery occurred, with median neutrophil and platelet engraftment at D+13 and D+12, respectively. No dose limiting toxicities were reported. Temferon-derived cells were found within 14 days post treatment and persisted albeit at lower levels in the long-term. Five deaths occurred: one at +478, three at +322, +340 and +402 days due to PD, and the fourth at +60 due to complications following the conditioning regimen. Eight patients had PD (-12 to +239). SAEs include respiratory tract infections, pulmonary embolism, CMV and C.Diff infections, febrile neutropenia, hemiparesis, seizure, brain abscess, worsening of performance status and respiratory failure compatible with ASCT, concomitant medications and PD. Four patients underwent second surgery. Recurrent tumors had gene-marked cells present and increased expression of ISGs compared to diagnosis, indicative of local IFNa release by TEMs. In one patient, a stable lesion had a higher proportion of T cells and TEMs within the myeloid infiltrate and an increased IFN-response signature than in a progressing lesion. Characterization of T-cell immune repertoire suggests the expansion of tumor-associated clones. TME characterization by scRNA and TCR sequencing is ongoing. Interim results show that Temferon is well tolerated, with no dose limiting toxicities identified to date and provide initial evidence of potential immune system activation within the TME.

Recent developments in modern agri‐food markets have called into question the predictive capability of the traditional models and tools used by agricultural economists to assess and measure market power. As a result, agricultural economists are on a quest for novel methods and approaches that surpass the traditional market power construct. In this article, we present an essential overview of the evolution of the literature concerning power relationships in agrifood value chains. The goal of this review is to emphasize recent conceptual and empirical approaches, as well as highlight topics of interest that are likely to shape the direction of future analyses of market power and bargaining in the agrifood sector. [EconLit citations: L1, L2, L3, Q13, Q18].

[][1] The sad news that Ralph A. Reisfeld, PhD, passed away on December 6, 2020 reached us early this year, and we would like to reflect on Ralph's long career and distinctive contribution to biomedical science and in particular to the field of cancer research. Ralph was well known