La determinación de los salarios en el mercado de trabajo : el caso de Islandia y Noruega

En este trabajo examinamos el comportamiento del mercado de trabajo de Islandia y Noruega en las últimas décadas y en particular analizamos el proceso de formación salarial desde la óptica de la teoría de la reacción en cadena (TRC). Nuestras ecuaciones de salarios estimadas indican que en ambos países los salarios vienen determinados por los mismos factores: a) un componente que muestra la influencia que ejercen las decisiones del pasado en las decisiones presentes, b) la productividad del empleo, c) los subsidios de desempleo y d) la tasa de desempleo. Así, nuestros resultados refuerzan la hipótesis de la TRC sobre la importancia de utilizar conjuntamente variables estacionarias (instituciones del mercado de trabajo) y variables no estacionarias (variables con tendencia) para analizar el comportamiento del mercado de trabajo o de alguno de sus componentes, como en este caso el proceso de formación salarial.Fil: Salvador, Pablo F.. Consejo Nacional de Investigaciones Científicas y TécnicasFil: Salvi, Mauro. Universidad Nacional de Cuy

Sensitivity limits of a Raman atom interferometer as a gravity gradiometer

We evaluate the sensitivity of a dual cloud atom interferometer to the measurement of vertical gravity gradient. We study the influence of most relevant experimental parameters on noise and long-term drifts. Results are also applied to the case of doubly differential measurements of the gravitational signal from local source masses. We achieve a short term sensitivity of 3*10^(-9) g/Hz^(-1/2) to differential gravity acceleration, limited by the quantum projection noise of the instrument. Active control of the most critical parameters allows to reach a resolution of 5*10^(-11) g after 8000 s on the measurement of differential gravity acceleration. The long term stability is compatible with a measurement of the gravitational constant G at the level of 10^(-4) after an integration time of about 100 hours.Comment: 19 pages, 20 figure

Role of Atypical Chemokine Receptors in Microglial Activation and Polarization.

Inflammatory reactions occurring in the central nervous system (CNS), known as neuroinflammation, are key components of the pathogenic mechanisms underlying several neurological diseases. The chemokine system plays a crucial role in the recruitment and activation of immune and non-immune cells in the brain, as well as in the regulation of microglia phenotype and function. Chemokines belong to a heterogeneous family of chemotactic agonists that signal through the interaction with G protein-coupled receptors (GPCRs). Recently, a small subset of chemokine receptors, now identified as “atypical chemokine receptors” (ACKRs), has been described. These receptors lack classic GPCR signaling and chemotactic activity and are believed to limit inflammation through their ability to scavenge chemokines at the inflammatory sites. Recent studies have highlighted a role for ACKRs in neuroinflammation. However, in the CNS, the role of ACKRs seems to be more complex than the simple control of inflammation. For instance, CXCR7/ACKR3 was shown to control T cell trafficking through the regulation of CXCL12 internalization at CNS endothelial barriers. Furthermore, D6/ACKR2 KO mice were protected in a model of experimental autoimmune encephalomyelitis (EAE). D6/ACKR2 KO showed an abnormal accumulation of dendritic cells at the immunization and a subsequent impairment in T cell priming. Finally, CCRL2, an ACKR-related protein, was shown to play a role in the control of the resolution phase of EAE. Indeed, CCRL2 KO mice showed exacerbated, non- resolving disease with protracted inflammation and increased demyelination. This phenotype was associated with increased microglia and macrophage activation markers and imbalanced M1 vs. M2 polarization. This review will summarize the current knowledge on the role of the ACKRs in neuroinflammation with a particular attention to their role in microglial polarization and function

The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis

Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis challenges, particularly in the field of digital pathology. The variety of image analysis tasks in the context of deep learning includes classification (e.g., healthy vs. cancerous tissue), detection (e.g., lymphocytes and mitosis counting), and segmentation (e.g., nuclei and glands segmentation). The majority of recent machine learning methods in digital pathology have a pre- and/or post-processing stage which is integrated with a deep neural network. These stages, based on traditional image processing methods, are employed to make the subsequent classification, detection, or segmentation problem easier to solve. Several studies have shown how the integration of pre- and post-processing methods within a deep learning pipeline can further increase the model's performance when compared to the network by itself. The aim of this review is to provide an overview on the types of methods that are used within deep learning frameworks either to optimally prepare the input (pre-processing) or to improve the results of the network output (post-processing), focusing on digital pathology image analysis. Many of the techniques presented here, especially the post-processing methods, are not limited to digital pathology but can be extended to almost any image analysis field

Effects of defoliation at fruit set on vine physiology and berry composition in cabernet sauvignon grapevines

Grapevine canopy defoliation is a fundamentally important technique for the productivity and quality of grapes. Leaf removal is a pivotal operation on high-density vines which aims to improve air circulation, light exposure, and leaf gas exchange. The effects of leaf removal (LR) on vine physiology and berry composition in Cabernet Sauvignon grapevines were studied during the 2018–2019 growing season in the Bolgheri area, Tuscany, Italy. The basal leaves were removed at fruit set at two severity levels (removal of four basal leaves of each shoot (LR4) and removal of eight basal leaves (LR8)). The two treatments were compared with the not defoliated control (CTRL). The following physiological parameters of vines were measured: leaf gas exchange, leaf water potential, chlorophyll fluorescence and indirect chlorophyll content. The results showed that defoliation increased single leaf photosynthesis. In addition, qualitative grape parameters (phenolic and technological analyses) and daytime and night-time berry temperature were studied. The results showed that leaf removal had an impact on total soluble solids (°Brix), titratable acidity, and pH. The LR8-treated grapes had higher titratable acidity, while those in the LR4 treatment had higher °Brix and extractable anthocyanin and polyphenol content. Berry weight was not significantly influenced by the timing and severity of basal defoliation. Therefore, this research aims to investigate the effects of defoliation at the fruit set on vines performance

The relationship between seismic deformation and deep seated gravitational

This paper re-evaluates the origin of some peculiar patterns of ground deformation observed by space geodetic techniques during the two earthquakes of September 26th of the Colfiorito seismic sequence. The surface displacement field due to the fault dislocation, as modeled with the classic Okada elastic formulations, shows some areas with high residuals which cannot be attributed to unsimulated model complexities. The latter was investigated using geomorphological analysis, by recognising the geologic evidence of deep seated gravitational slope deformations (DSGSD) of the block-slide type. The shape and direction of the co-seismic ground displacement observed in these areas are correlated with the expected pattern of movement produced by the reactivation of the identified DSGSD. At least a few centimetres of negative Line of Sight ground displacement was determined for the Costa Picchio, Mt. Pennino, and Mt. Prefoglio areas. A considerable horizontal component of movement in the Costa Picchio DSGSD is evident from a qualitative analysis of ascending and descending interferograms. The timing of the geodetic data indicates that the ground movement occurred during the seismic shaking, and that it did not progress appreciably during the following months. In this work it has been verified the seismic triggering of DSGSD previously hypothesized by many authors. A further implication is that in the assessment of DSGSD hazard it is necessary to consider the seismic input as an important cause of acceleration of the deformation rates

The Sentinel-1 mission for the improvement of the scientific understanding and the operational monitoring of the seismic cycle

We describe the state of the art of scientific research on the earthquake cycle based on the analysis of Synthetic Aperture Radar (SAR) data acquired from satellite platforms. We examine the achievements and the main limitations of present SAR systems for the measurement and analysis of crustal deformation, and envision the foreseeable advances that the Sentinel-1 data will generate in the fields of geophysics and tectonics. We also review the technological and scientific issues which have limited so far the operational use of satellite data in seismic hazard assessment and crisis management, and show the improvements expected from Sentinel-1 dat

Comparison of Histogram-based Textural Features between Cancerous and Normal Prostatic Tissue in Multiparametric Magnetic Resonance Images

In the last decade, multiparametric magnetic resonance imaging (mpMRI) has been expanding its role in prostate cancer detection and characterization. In this work, 19 patients with clinically significant peripheral zone (PZ) tumours were studied. Tumour masks annotated on the whole-mount histology sections were mapped on T2-weighted (T2w) and diffusion-weighted (DW) sequences. Gray-level histograms of tumoral and normal tissue were compared using six first-order texture features. Multivariate analysis of variance (MANOVA) was used to compare group means. Mean intensity signal of ADC showed the highest showed the highest area under the receiver operator characteristics curve (AUC) equal to 0.85. MANOVA analysis revealed that ADC features allows a better separation between normal and cancerous tissue with respect to T2w features (ADC: P = 0.0003, AUC = 0.86; T2w: P = 0.03, AUC = 0.74). MANOVA proved that the combination of T2-weighted and apparent diffusion coefficient (ADC) map features increased the AUC to 0.88. Histogram-based features extracted from invivo mpMRI can help discriminating significant PZ PCa