Does Common Agricultural Policy Reduce Farm Labour Migration? A Panel Data Analysis Across EU Regions

This paper deals with the determinants of labour out-migration from agriculture across 153 EU regions over the 1990-2008 period. The central aim is to shed light on the role played by CAP payments on this important adjustment process. Using static and dynamic panel data methods, we show that standard neo-classic drivers, like the relative income and the relative labour share, represented significant determinants of the inter-sectoral migration of the agricultural labour. Overall, CAP payments have contributed significantly to job creation in agriculture, although the magnitude of the economic effect is quite small. Moreover, Pillar I subsidies have exerted an effect from three to five times stronger than Pillar II payments.Out-farm Migration, CAP Payments, Labour Markets, Panel Data Analysis, Agricultural and Food Policy, Labor and Human Capital, Q12, Q18, O13, J21, J43, J60,

Li-Ion Batteries: A Review of a Key Technology for Transport Decarbonization

Lithium ion batteries are experiencing an increased success thanks to their interesting performances, in particular for electric vehicles applications. Their continuous technological improvements in the last years are providing higher energy density and lower manufacturing costs. However, the environmental performance of their supply chain is of paramount importance to guarantee a cleaner alternative to fossil-based solutions on the entire life cycle of the applications. This paper carries out a comprehensive review on the main aspects related to Li-ion batteries manufacturing, to support the readers in understanding the complexity of the subject and the main challenges and opportunities for the future developments of this technology. The paper discusses the expected future demand of batteries; the main aspects related to the supply chain, including existing assets, input materials and alternative technologies; the end-of-life of batteries; the environmental impacts; and the main geopolitical implications

Off-farm Labour Decision of Italian Farm Operators. Factor Markets Working Document No. 61, August 2013

This paper analyses the factors affecting off-farm labour decisions of Italian farm operators. Using micro-level data from the Farm Business Survey (REA) over the pre- and post-2003 CAP reform periods, we investigated the impact that operator, family, farm and market characteristics exert on these choices. Among other things, the paper focuses also on the differential impact of those variables for operators of smaller and larger holdings. The main results suggest that operator and family characteristics have a significant impact on the decision to participate in off-farm work more for smaller than for bigger farms. By contrast, farm characteristics are more relevant variables for bigger farms. In particular, decoupled farm payments, by increasing the marginal productivity of farm labour, lower the probability of working off the farm only in bigger farms, while coupled subsidies in pre-reform years do not have a significant impact on labour decisions. Finally, we show that, after accounting for the standard covariates, local and territorial labour market characteristics generally have a low effect on off-farm work operators’ choices

Does the Common Agricultural Policy Reduce Farm Labour Migration? Panel data analysis across EU regions. Factor Markets Working Paper No. 28, July 2012

This paper deals with the determinants of labour out-migration from agriculture across 149 EU regions over the 1990–2008 period. The central aim is to shed light on the role played by payments from the common agricultural policy (CAP) on this important adjustment process. Using static and dynamic panel data estimators, we show that standard neoclassical drivers, like relative income and the relative labour share, represent significant determinants of the intersectoral migration of agricultural labour. Overall, CAP payments contributed significantly to job creation in agriculture, although the magnitude of the economic effect was rather moderate. We also find that pillar I subsidies exerted an effect approximately two times greater than that of pillar II payments

From genotype to phenotype in Arabidopsis thaliana: in-silico genome interpretation predicts 288 phenotypes from sequencing data

In many cases, the unprecedented availability of data provided by high-throughput sequencing has shifted the bottleneck from a data availability issue to a data interpretation issue, thus delaying the promised breakthroughs in genetics and precision medicine, for what concerns Human genetics, and phenotype prediction to improve plant adaptation to climate change and resistance to bioagressors, for what concerns plant sciences. In this paper, we propose a novel Genome Interpretation paradigm, which aims at directly modeling the genotype-to-phenotype relationship, and we focus on A. thaliana since it is the best studied model organism in plant genetics. Our model, called Galiana, is the first end-to-end Neural Network (NN) approach following the genomes in/phenotypes out paradigm and it is trained to predict 288 real-valued Arabidopsis thaliana phenotypes from Whole Genome sequencing data. We show that 75 of these phenotypes are predicted with a Pearson correlation ≥0.4, and are mostly related to flowering traits. We show that our end-to-end NN approach achieves better performances and larger phenotype coverage than models predicting single phenotypes from the GWAS-derived known associated genes. Galiana is also fully interpretable, thanks to the Saliency Maps gradient-based approaches. We followed this interpretation approach to identify 36 novel genes that are likely to be associated with flowering traits, finding evidence for 6 of them in the existing literature

Patterns and Determinants of Off-Farm Migration: Transfer frictions and persistency of relative income gaps. Factor Markets Working Papers No. 36, February 2013

The inter-sectoral migration of agricultural labour is a complex but fundamental process of economic development largely affected by the growth of agricultural productivity and the evolution of the agricultural relative income gap. Theory and some recent anecdotal evidence suggest that as an effect of large fixed and sunk costs of out-farm migration, the productivity gap between the agricultural and non-agricultural sectors should behave non-monotonically or following a U-shaped evolution during economic development. Whether or not this relationship holds true across a sample of 38 developing and developed countries and across more than 200 EU regions was empirically tested. Results strongly confirm this relationship, which also emphasises the role played by national agricultural policy

Cancer profiles by affinity propagation

The affinity propagation algorithm is applied to a problem of breast cancer subtyping using traditional biologic markers. The algorithm provides a procedure to determine the number of profiles to be considered. A well know breast cancer case series was used to compare the results of the affinity propagation with the results obtained with standard algorithms and indexes for the optimal choice of the number of clusters. Results from affinity propagation are consistent with the results already obtained having the advantage of providing an indication about the number of clusters

MiR-33a Controls hMSCS Osteoblast Commitment Modulating the Yap/Taz Expression Through EGFR Signaling Regulation

Mesenchymal stromal cells (hMSCs) display a pleiotropic function in bone regeneration. The signaling involved in osteoblast commitment is still not completely understood, and that determines the failure of current therapies being used. In our recent studies, we identified two miRNAs as regulators of hMSCs osteoblast differentiation driving hypoxia signaling and cytoskeletal reorganization. Other signalings involved in this process are epithelial to mesenchymal transition (EMT) and epidermal growth factor receptor (EGFR) signalings through the regulation of Yes-associated protein (YAP)/PDZ-binding motif (TAZ) expression. In the current study, we investigated the role of miR-33a family as a (i) modulator of YAP/TAZ expression and (ii) a regulator of EGFR signaling during osteoblast commitments. Starting from the observation on hMSCs and primary osteoblast cell lines (Nh-Ost) in which EMT genes and miR-33a displayed a specific expression, we performed a gain and loss of function study with miR-33a-5p and 3p on hMSCs cells and Nh-Ost. After 24 h of transfections, we evaluated the modulation of EMT and osteoblast genes expression by qRT-PCR, Western blot, and Osteoimage assays. Through bioinformatic analysis, we identified YAP as the putative target of miR-33a-3p. Its role was investigated by gain and loss of function studies with miR-33a-3p on hMSCs; qRT-PCR and Western blot analyses were also carried out. Finally, the possible role of EGFR signaling in YAP/TAZ modulation by miR-33a-3p expression was evaluated. Human MSCs were treated with EGF-2 and EGFR inhibitor for different time points, and qRT-PCR and Western blot analyses were performed. The above-mentioned methods revealed a balance between miR-33a-5p and miR-33a-3p expression during hMSCs osteoblast differentiation. The human MSCs phenotype was maintained by miR-33a-5p, while the maintenance of the osteoblast phenotype in the Nh-Ost cell model was permitted by miR-33a-3p expression, which regulated YAP/TAZ through the modulation of EGFR signaling. The inhibition of EGFR blocked the effects of miR-33a-3p on YAP/TAZ modulation, favoring the maintenance of hMSCs in a committed phenotype. A new possible personalized therapeutic approach to bone regeneration was discussed, which might be mediated by customizing delivery of miR-33a in simultaneously targeting EGFR and YAP signaling with combined use of drugs

an interpretable low complexity machine learning framework for robust exome based in silico diagnosis of crohn s disease patients

Abstract Whole exome sequencing (WES) data are allowing researchers to pinpoint the causes of many Mendelian disorders. In time, sequencing data will be crucial to solve the genome interpretation puzzle, which aims at uncovering the genotype-to-phenotype relationship, but for the moment many conceptual and technical problems need to be addressed. In particular, very few attempts at the in-silico diagnosis of oligo-to-polygenic disorders have been made so far, due to the complexity of the challenge, the relative scarcity of the data and issues such as batch effects and data heterogeneity, which are confounder factors for machine learning (ML) methods. Here, we propose a method for the exome-based in-silico diagnosis of Crohn's disease (CD) patients which addresses many of the current methodological issues. First, we devise a rational ML-friendly feature representation for WES data based on the gene mutational burden concept, which is suitable for small sample sizes datasets. Second, we propose a Neural Network (NN) with parameter tying and heavy regularization, in order to limit its complexity and thus the risk of over-fitting. We trained and tested our NN on 3 CD case-controls datasets, comparing the performance with the participants of previous CAGI challenges. We show that, notwithstanding the limited NN complexity, it outperforms the previous approaches. Moreover, we interpret the NN predictions by analyzing the learned patterns at the variant and gene level and investigating the decision process leading to each prediction

How good Neural Networks interpretation methods really are? A quantitative benchmark

Saliency Maps (SMs) have been extensively used to interpret deep learning models decision by highlighting the features deemed relevant by the model. They are used on highly nonlinear problems, where linear feature selection (FS) methods fail at highlighting relevant explanatory variables. However, the reliability of gradient-based feature attribution methods such as SM has mostly been only qualitatively (visually) assessed, and quantitative benchmarks are currently missing, partially due to the lack of a definite ground truth on image data. Concerned about the apophenic biases introduced by visual assessment of these methods, in this paper we propose a synthetic quantitative benchmark for Neural Networks (NNs) interpretation methods. For this purpose, we built synthetic datasets with nonlinearly separable classes and increasing number of decoy (random) features, illustrating the challenge of FS in high-dimensional settings. We also compare these methods to conventional approaches such as mRMR or Random Forests. Our results show that our simple synthetic datasets are sufficient to challenge most of the benchmarked methods. TreeShap, mRMR and LassoNet are the best performing FS methods. We also show that, when quantifying the relevance of a few non linearly-entangled predictive features diluted in a large number of irrelevant noisy variables, neural network-based FS and interpretation methods are still far from being reliable