Multiple Regimes in Cross-Region Growth Regressions with Spatial Dependence: A Parametric and a Semi-parametric Approach

This paper studies the distribution dynamics of development across European regions over the period 1975-2000. Regional development is measured in terms of both per capita GDP (Y/P) and its components: labour productivity and employment ratio (that in turn can be decomposed in terms of activity and unemployment rate). The Core/Periphery pattern in the European Union is firstly investigated and a comparative analysis in terms of income, productivity, employment and unemployment rates of the two partitions is carried out. Moreover, for each variable as well as for each partition, a nonparametric beta convergence analysis is applied. Synthetically, the results confirm the lack of regional convergence in per capita incomes, the presence of a negative quasi-linear relationship between growth rates and initial levels of labour productivity and a U-shaped relationship between growth rates and initial levels of unemployment rates. As it is well known, however, b-convergence analysis does not allow any test of multiple equilibria, such as “emerging twin peaks”, in the growth process. Equilibrium multiplicity can be properly assessed by using nonparametric techniques of analysis of the cross-regional distribution. In particular, a way to quantify the intra-distribution dynamics is the multivariate kernel, which estimates the joint density of regional income, productivity and (un)employment distribution at time t0 and t0+t. The results of this analysis suggest that over the period considered the regional growth pattern in Europe has followed a polarisation process rather than a convergence path. This appears particularly true in the case of per capita incomes and unemployment rates. Finally, in order to “explain” polarisation, conditional multivariate kernels are estimated. In particular, the role of spatial contiguity and regional sectoral specialisation is investigated.

Pairs Trading in the Index Option Market

We test the Index options market efficiency by means of a statistical arbitrage strategy, i.e. pairs trading. Using data on five Index Option Market of the Euro Area, we first identify any potential option mispricing based on deviations from the long-run relationship linking their implied volatilities. Then, we evaluate the profitability of a simple pair trading strategy on the mispriced options. Despite the signals of potential mispricing are frequent, the statistical arbitrage does not produce significant profits, thus providing evidence in support of Index Option market efficiency. The results, which remain unchanged in a variety of robustness checks, also prove that the observed profits are strongly associated to the moneyness of the options traded while they do not correlate to options’ maturity or to financial market turbulence

Bioelectrochemical hydrogen production with hydrogenophilic dechlorinating bacteria as electrocatalytic agents

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UCN-01 enhances cytotoxicity of irinotecan in colorectal cancer stem-like cells by impairing DNA damage response

Colorectal cancer (CRC) is one of the most common and lethal cancers worldwide. Despite recent progress, the prognosis of advanced stage CRC remains poor, mainly because of cancer recurrence and metastasis. The high morbidity and mortality of CRC has been recently ascribed to a small population of tumor cells that hold the potential of tumor initiation, i.e. cancer stem cells (CSCs), which play a pivotal role in cancer recurrence and metastasis and are not eradicated by current therapy. We screened CRC-SCs in vitro with a library of protein kinase inhibitors and showed that CRC-SCs are resistant to specific inhibition of the major signaling pathways involved in cell survival and proliferation. Nonetheless, broad-spectrum inhibition by the staurosporin derivative UCN-01 blocks CRC-SC growth and potentiates the activity of irinotecan in vitro and in vivo CRC-SC-derived models. Reverse-Phase Protein Microarrays (RPPA) revealed that, albeit CRC-SCs display individual phospho-proteomic profiles, sensitivity of CRC-SCs to UCN-01 relies on the interference with the DNA damage response mediated by Chk1. Combination of LY2603618, a specific Chk1/2 inhibitor, with irinotecan resulted in a significant reduction of CRC-SC growth in vivo, confirming that irinotecan treatment coupled to inhibition of Chk1 represents a potentially effective therapeutic approach for CRC treatment

Modularity as a strategy for medical design

In the last decade, collaborations between Design and the biomedical field have become increasingly frequent and effective, thanks to their shared focus on user-centred innovation. Design comes into play when unresolved challenges arise, mainly related to the usability of biomedical products, especially in the field of physical and rehabilitative medicine, where aids used for therapeutic treatments and exercises are often inadequate for scientific advancements and the complex needs of patients and therapists. Through a critical analysis of international best practices, this paper promotes a design approach based on modularity and potential kits for neuro-psychological, orthopaedic, and speech rehabilitation medicine. In this context, the modularity strategy offers an opportunity to develop new design-driven solutions for more flexible, adaptable, ergonomic, comfortable, and sustainable tools.   Article info Received: 10/09/2023; Revised: 10/10/2023; Accepted: 20/10/202

Marginal quality of a full-body bulk-fill composite placed with an universal adhesive system in etch-and-rinse and self-etch mode : an in vitrostudy

Marginal seal of a nanohybrid bulk-fill composite compared to a nanohybrid conventional composite, using a universal adhesive (UA) applied in etch-and-rinse (ER) and self-etch (SE) mode was investigated. Thirty-six intact molars were selected and two s

Matching single cells across modalities with contrastive learning and optimal transport.

Understanding the interactions between the biomolecules that govern cellular behaviors remains an emergent question in biology. Recent advances in single-cell technologies have enabled the simultaneous quantification of multiple biomolecules in the same cell, opening new avenues for understanding cellular complexity and heterogeneity. Still, the resulting multimodal single-cell datasets present unique challenges arising from the high dimensionality and multiple sources of acquisition noise. Computational methods able to match cells across different modalities offer an appealing alternative towards this goal. In this work, we propose MatchCLOT, a novel method for modality matching inspired by recent promising developments in contrastive learning and optimal transport. MatchCLOT uses contrastive learning to learn a common representation between two modalities and applies entropic optimal transport as an approximate maximum weight bipartite matching algorithm. Our model obtains state-of-the-art performance on two curated benchmarking datasets and an independent test dataset, improving the top scoring method by 26.1% while preserving the underlying biological structure of the multimodal data. Importantly, MatchCLOT offers high gains in computational time and memory that, in contrast to existing methods, allows it to scale well with the number of cells. As single-cell datasets become increasingly large, MatchCLOT offers an accurate and efficient solution to the problem of modality matching