Pappa Ante Portas: The effect of the husband's retirement on the wife's mental health in Japan

The \u201cRetired Husband Syndrome\u201d, that affects the mental health of wives of retired men around the world, has been anecdotally documented but never formally investigated. Using Japanese micro-data and the exogenous variation across cohorts in the maximum age of guaranteed employment induced by a 2006 Japanese reform, we estimate that the husband's earlier retirement significantly increases the probability that the wife reports symptoms related to the syndrome. We also find that retirement has a negative effect both on the household's economic situation and on the husband's own mental health, and that the higher economic distress contributes to reducing the wife's mental health

Does Postponing Minimum Retirement Age Improve Healthy Behaviours Before Retirement? Evidence from Middle-Aged Italian Workers.

By increasing the residual working horizon of employed individuals, pension reforms that rise minimum retirement age can affect individual investment in health-promoting behaviors before retirement. Using the expected increase in minimum retirement age induced by a 2004 Italian pension reform and a difference-in-differences research design, we show that middle-aged Italian males affected by the reform reacted to the longer working horizon by increasing regular exercise, with positive consequences for obesity and self-reported satisfaction with health

Training during recessions: recent European evidence

Abstract We use European Union Labour Force Survey data for the period 2005–2018 to investigate the cyclicality of training in Europe. Consistent with the view that firms use recessions as times to update skills, we find that training participation is moderately countercyclical for the employed. Within the not-employed group, this is true also for the unemployed, who are likely to be involved in public training programs during recessions, but not for the inactive, who may be affected by liquidity constraints

Bio-molecular diagnosis through Random Subspace Ensembles of Learning Machines.

Traditional clinical diagnostic approaches may sometimes fail in detecting tumors (Alizadeh et al. 2001). Several results showed that bio-molecular analysis of malignancies may help to better characterize malignancies (e.g. gene expression profiling). Information for supporting both diagnosis and prognosis of malignancies at bio-molecular level may be obtained from high-throughput biotechnologies (e.g. DNA microarray). Recent work on unsupervised analysis of complex bio-molecular data (Bertoni and Valentini, 2006) showed that random projections obeying the Johnson-Lindenstrauss lemma can be used for: \u2013 Discovering structures in bio-molecular data \u2013 Validating clustering results \u2013 Improving clustering results RS ensembles can improve the accuracy of biomolecular diagnosis characterized by very high dimensional data. They could be also easily applied to heterogeneous bio-molecular and clinical data. A new promising approach consists in combining state of the art feature (gene) selection methods and RS ensembles. RS ensembles are computationally intensive but can be easily parallelized using clusters of workstations (e.g. in a MPI framework)

UNIPred: Unbalance-aware Network Integration and Prediction of protein functions

Abstract The proper integration of multiple sources of data and the unbalance between annotated and unannotated proteins represent two of the main issues of the Automated Function Prediction (AFP) problem. Most of supervised and semi-supervised learning algorithms for AFP proposed in literature do not jointly consider these items, with a negative impact on both sensitivity and precision performances, due to the unbalance between annotated and unannotated proteins that characterize the majority of functional classes and to the specific and complementary information content embedded in each available source of data. We propose UNIPred (Unbalance-aware Network Integration and Prediction of protein functions), an algorithm that properly combines different biomolecular networks and predicts protein functions using parametric semi-supervised neural models. The algorithm explicitly takes into account the unbalance between unannotated and annotated proteins both to construct the integrated network and to predict protein annotations for each functional class. Full-genome and ontology-wide experiments with three Eukaryotic model organisms show that the proposed method compares favourably with state-of-the-art learning algorithms for AFP

UniPR1331: small Eph/ephrin antagonist beneficial in intestinal inflammation by interfering with type-B signaling

Eph receptors, comprising A and B classes, interact with cell-bound ephrins generating bidirectional signaling. Although mainly related to carcinogenesis and organogenesis, the role of Eph/ephrin system in inflammation is growingly acknowledged. Recently, we showed that EphA/ephrin-A proteins can modulate the acute inflammatory responses induced by mesenteric ischemia/reperfusion, while beneficial effects were granted by EphB4, acting as EphB/ephrin-B antagonist, in a murine model of Crohn’s disease (CD). Accordingly, we now aim to evaluate the effects of UniPR1331, a pan-Eph/ephrin antagonist, in TNBS-induced colitis and to ascertain whether UniPR1331 effects can be attributed to A- or B-type signaling interference. The potential anti-inflammatory action of UniPR1331 was compared to those of the recombinant proteins EphA2, a purported EphA/ephrin-A antagonist, and of ephrin-A1-Fc and EphA2-Fc, supposedly activating forward and reverse EphA/ephrin-A signaling, in murine TNBS-induced colitis and in stimulated cultured mononuclear splenocytes. UniPR1331 antagonized the inflammatory responses both in vivo, mimicking EphB4 protection, and in vitro; EphA/ephrin-A proteins were inactive or only weakly effective. Our findings represent a further proof-of-concept that blockade of EphB/ephrin-B signaling is a promising pharmacological strategy for CD management and highlight UniPR1331 as a novel drug candidate, seemingly working through the modulation of immune responses

Discovering multi–level structures in bio-molecular data through the Bernstein inequality

Background: The unsupervised discovery of structures (i.e. clusterings) underlying data is a central issue in several branches of bioinformatics. Methods based on the concept of stability have been recently proposed to assess the reliability of a clustering procedure and to estimate the ”optimal ” number of clusters in bio-molecular data. A major problem with stability-based methods is the detection of multi-level structures (e.g. hierarchical functional classes of genes), and the assessment of their statistical significance. In this context, a chi-square based statistical test of hypothesis has been proposed; however, to assure the correctness of this technique some assumptions about the distribution of the data are needed. Results: To assess the statistical significance and to discover multi-level structures in bio-molecular data, a new method based on Bernstein’s inequality is proposed. This approach makes no assumptions about the distribution of the data, thus assuring a reliable application to a large range of bioinformatics problems. Results with synthetic and DNA microarray data show the effectiveness of the proposed method. Conclusions: The Bernstein test, due to its loose assumptions, is more sensitive than the chi-square test to the detection of multiple structures simultaneously present in the data. Nevertheless it is less selective, that is subject to more false positives, but adding independence assumptions, a more selective variant of the Bernstein inequality-based test is also presented. The proposed methods can be applied to discover multiple structures and to assess their significance in different types of bio-molecular data

IRF4 Mediates the Oncogenic Effects of STAT3 in Anaplastic Large Cell Lymphomas

Systemic anaplastic large cell lymphomas (ALCL) are a category of T-cell non-Hodgkin’s lymphomas which can be divided into anaplastic lymphoma kinase (ALK) positive and ALK negative subgroups, based on ALK gene rearrangements. Among several pathways aberrantly activated in ALCL, the constitutive activation of signal transducer and activator of transcription 3 (STAT3) is shared by all ALK positive ALCL and has been detected in a subgroup of ALK negative ALCL. To discover essential mediators of STAT3 oncogenic activity that may represent feasible targets for ALCL therapies, we combined gene expression profiling analysis and RNA interference functional approaches. A shRNA screening of STAT3-modulated genes identified interferon regulatory factor 4 (IRF4) as a key driver of ALCL cell survival. Accordingly, ectopic IRF4 expression partially rescued STAT3 knock-down effects. Treatment with immunomodulatory drugs (IMiDs) induced IRF4 down regulation and resulted in cell death, a phenotype rescued by IRF4 overexpression. However, the majority of ALCL cell lines were poorly responsive to IMiDs treatment. Combination with JQ1, a bromodomain and extra-terminal (BET) family antagonist known to inhibit MYC and IRF4, increased sensitivity to IMiDs. Overall, these results show that IRF4 is involved in STAT3-oncogenic signaling and its inhibition provides alternative avenues for the design of novel/combination therapies of ALCL