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SUMMARYCapturing agricultural heterogeneity through the analysis of farm typologies is key with regard to the design of sustainable policies and to the adoptability of new technologies. An optimal balance needs to be found between, on the one hand, the requirement to consider local stakeholder and expert knowledge for typology identification, and on the other hand, the need to identify typologies that transcend the local boundaries of single studies and can be used for comparisons. In this paper, we propose a method that supports expert-driven identification of farm typologies, while at the same time keeping the characteristics of objectivity and reproducibility of statistical tools. The method uses a range of multivariate analysis techniques and it is based on a protocol that favours the use of stakeholder and expert knowledge in the process of typology identification by means of visualization of farm groups and relevant statistics. Results of two studies in Zimbabwe and Kenya are shown. Findings obtained with the method proposed are contrasted with those obtained through a parametric method based on latent class analysis. The method is compared to alternative approaches with regard to stakeholder-orientation and statistical reliability.

SUMMARYIn this paper, a Conditional Linear Gaussian Network (CLGN) model is built for a twoyear experiment on Tuscan Sangiovese grapes involving canopy management techniques (number of buds, defoliation and bunch thinning) and harvest time (technological and late harvest). We found that the impact of the considered treatments on the color of wine can be predicted still in the vegetative season of the grapevine; the best treatments to obtain wines with good structure are those with a low number of buds; the best treatments to obtain fresh wines suitable for young consumers are those with technological rather than late harvest, preferably with a high number of buds, and anyway with both defoliation and bunch thinning not performed.

IntroductionCirculating tumor DNA (ctDNA) correlates with the response to therapy in different types of cancer. However, in patients with locally advanced rectal cancer (LARC), little is known about how ctDNA levels change with neoadjuvant chemoradiation (Na-ChRT) and how they correlate with treatment response. This work aimed to explore the value of serial liquid biopsies in monitoring response after Na-ChRT with the hypothesis that this could become a reliable biomarker to identify patients with a complete response, candidates for non-operative management.Materials and MethodsTwenty-five consecutive LARC patients undergoing long-term Na-ChRT therapy were included. Applying next-generation sequencing (NGS), we characterized DNA extracted from formalin-fixed paraffin embedded diagnostic biopsy and resection tissue and plasma ctDNA collected at the following time points: the first and last days of radiotherapy (T0, Tend), at 4 (T4), 7 (T7) weeks after radiotherapy, on the day of surgery (Top), and 3–7 days after surgery (Tpost-op). On the day of surgery, a mesenteric vein sample was also collected (TIMV). The relationship between the ctDNA at those time-points and the tumor regression grade (TRG) of the surgical specimen was statistically explored.ResultsWe found no association between the disappearance of ctDNA mutations in plasma samples and pathological complete response (TRG1) as ctDNA was undetectable in the majority of patients from Tend on. However, we observed that the poor (TRG 4) response to Na-ChRT was significantly associated with a positive liquid biopsy at the Top.ConclusionsctDNA evaluation by NGS technology may identify LARC patients with poor response to Na-ChRT. In contrast, this technique does not seem useful for identifying patients prone to developing a complete response.

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SummaryCarcinogenesis is a multi-step process involving genetic alterations and non-genotoxic mechanisms. The in vitro cell transformation assay allows the monitoring of the neoplastic phenotype by foci formation in suitable cells (e.g. C3H10T1/2 mouse embryo fibroblasts) showing aberrant morphology of massive build-up, polar and multi-layered densely stained cells. The classification of transformed foci in C3H cells relies on light microscopy scoring by a trained human expert based on standard rules. This procedure is time-consuming and prone, in some cases, to subjectivity, thereby leading to possible over-or under-estimation of the carcinogenic potential of tested compounds. Herewith we describe the in vitro neoplastic transformation induced by B[a]P and CdCl 2 , and the development of a foci classifier based on image analysis and statistical classification. The image analysis system, which relies on 'spectrum enhancement', is quantitative and extracts descriptors of foci texture and structure. The statistical classification method is based on the Random Forest algorithm. We obtained a classifier trained by using expert's supervision with a 20% classification error. The proposed method could serve as a basis to automate the in vitro cell transformation assay.

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