Additional index words. Lycium barbarum, axillary shoot proliferation, in vitro and ex vitro rooting, acclimatization, growth regulatorsAbstract. An accurate protocol for the in vitro propagation of a commercial wolfberry (Lycium barbarum L.) cv. Nixia 1 has been developed through axillary shoot proliferation. Driver and Kuniyuki Walnut (DKW) medium supplemented with 6-benzylaminopurine (BAP; 0.5 mg/L) and sucrose 3% w/v gave the best results compared with other basal media tested, with significantly improved production of multiple shoots direct from nodal segment explants, resulting in an average of 6.73 shoots/explant with an average of 7.45 nodes/shoot that would potentially form new explants. Rooting of shoot explants was carried out both in vitro and ex vitro with 0.5 and 1 mg/L of indole-3-butyric acid (IBA), with or without adding putrescine (160 mg/L). In all cases, rooting efficiency resulted very high, and putrescine was effective only when combined with a low concentration of auxin. Plantlets were hardened off in jiffy pots under greenhouse conditions, with a survival rate of more than 90%. Ex vitro rooting, performed by dipping in an aqueous solution of IBA 100 mg/L, is the preferred technique not only because rooting and acclimatization are very high but also reducing micropropagation to one phase is more economical.

Background: The new paradigm of precision medicine brought an increasing interest in survival prediction based on the integration of multi-omics and multi-sources data. Several models have been developed to address this task, but their performances are widely variable depending on the specific disease and are often poor on noisy datasets, such as in the case of non-small cell lung cancer (NSCLC). Objective: The aim of this work is to introduce a novel computational approach, named multi-omic two-layer SVM (mtSVM), and to exploit it to get a survival-based risk stratification of NSCLC patients from an ongoing observational prospective cohort clinical study named PROMOLE. Methods: The model implements a model-based integration by means of a two-layer feed-forward network of FastSurvivalSVMs, and it can be used to get individual survival estimates or survival-based risk stratification. Despite being designed for NSCLC, its range of applicability can potentially cover the full spectrum of survival analysis problems where integration of different data sources is needed, independently of the pathology considered. Results: The model is here applied to the case of NSCLC, and compared with other state-of-the-art methods, proving excellent performance. Notably, the model, trained on data from The Cancer Genome Atlas (TCGA), has been validated on an independent cohort (from the PROMOLE study), and the results were consistent. Gene-set enrichment analysis of the risk groups, as well as exome analysis, revealed well-defined molecular profiles, such as a prognostic mutational gene signature with potential implications in clinical practice.

Recent advances in machine learning research, combined with the reduced sequencing costs enabled by modern next-generation sequencing, paved the way to the implementation of precision medicine through routine multi-omics molecular profiling of tumours. Thus, there is an emerging need of reliable models exploiting such data to retrieve clinically useful information. Here, we introduce an original consensus clustering approach, overcoming the intrinsic instability of common clustering methods based on molecular data. This approach is applied to the case of non-small cell lung cancer (NSCLC), integrating data of an ongoing clinical study (PROMOLE) with those made available by The Cancer Genome Atlas, to define a molecular-based stratification of the patients beyond, but still preserving, histological subtyping. The resulting subgroups are biologically characterized by well-defined mutational and gene-expression profiles and are significantly related to disease-free survival (DFS). Interestingly, it was observed that (1) cluster B, characterized by a short DFS, is enriched in KEAP1 and SKP2 mutations, that makes it an ideal candidate for further studies with inhibitors, and (2) over- and under-representation of inflammation and immune systems pathways in squamous-cell carcinomas subgroups could be potentially exploited to stratify patients treated with immunotherapy.

In a rapidly urbanizing world, urban agriculture (UA) represents an opportunity for improving food supply, health conditions, local economy, social integration, and environmental sustainability altogether. While a diversity of farming systems is encountered in the different world regions, it is estimated that about a third of urban dwellers is involved worldwide in the agro-food sector. In recent times, UA projects have sprouted across the world, both guided and promoted by governments and born by bottom-up community based initiatives. Accordingly, the concept of edible urban landscapes (edible cities, foodscapes) is today finding application all over the world. In order to facilitate a wider uptake of innovative policies and tools for the promotion of the sustainable goals associated with UA, it is crucial to create awareness on both institutional actors and the civil society through innovative and interdisciplinary approaches. The international student challenge UrbanFarm2019 aimed at tackling the current need for cooperation between different disciplines by bringing together students from different fields of study into international teams addressing the regeneration of three vacant urban spaces in Italy. The chosen locations included a former agricultural farm absorbed by the urban sprawl, a factory of domestic appliances that moved production abroad and a primary school in the Alps that was closed due to ageing of the local population. The three locations shared their current vacant status and the fact that they all constitute a cost and a missed opportunity for their cities. The UrbanFarm2019 challenge aimed at showing that another use for these spaces is possible, overall contributing to creating cities that are more attractive, livable, inclusive, and sustainable. To reach this target, young minds from all over the world were engaged in international and interdisciplinary teams. The challenge became an opportunity to link viewpoints and approaches while integrating state-ofthe-art technologies and design for urban farming and urban planning. Innovative ideas, visions and approaches were brought together by teams of students with enthusiasm and dedication. Starting from these project ideas, local administrators and urban planners will have tools to foster sustainability in their cities. Beyond the elevation of project quality, the major achievement of the UrbanFarm competition stands upon the geographical distribution and expertise covered by the participating teams. The UrbanFarm international student challenge, achieved to engage a network of experts and UA practitioners from universities from all over the world in the evaluation of 35 projects prepared by teams involving more than 130 students. Looking at the projects, it clearly appears how competences were successfully integrated and communicated in both project redaction and visual materials.

Recently, there has been a growing interest in bioinformatics toward the adoption of increasingly complex machine learning models for the analysis of next-generation sequencing data with the goal of disease subtyping (i.e., patient stratification based on molecular features) or risk-based classification for specific endpoints, such as survival. With gene-expression data, a common approach consists in characterising the emerging groups by exploiting a differential expression analysis, which selects relevant gene sets coupled with pathway enrichment analysis, providing an insight into the underlying biological processes. However, when non-linear machine learning models are involved, differential expression analysis could be limiting since patient groupings identified by the model could be based on a set of genes that are hidden to differential expression due to its linear nature, affecting subsequent biological characterisation and validation. The aim of this study is to provide a proof-of-concept example demonstrating such a limitation. Moreover, we suggest that this issue could be overcome by the adoption of the innovative paradigm of eXplainable Artificial Intelligence, which consists in building an additional explainer to get a trustworthy interpretation of the model outputs and building a reliable set of genes characterising each group, preserving also non-linear relations, to be used for downstream analysis and validation.