Individuazione del rischio di fallimento via panel analysis

Obiettivo del presente lavoro \ue8 la calibrazione di un metodo di stima della probabilit\ue0 di insolvenza per le imprese manifatturiere di dimensioni medio-grandi. Dopo aver verificato il grado di adeguatezza del modello Z-score di Altman su un particolare collettivo di riferimento, si affronteranno alcune note criticit\ue0 di tale approccio, dalla equipartizione della numerosit\ue0 dei sottocampioni fino alla selezione di nuove variabili esplicative dell\u2019insolvenza. Ai fini della definizione di un nuovo modello, un approccio di stima di tipo panel ad effetti fissi consentir\ue0 la identificazione di variabili esplicative della probabilit\ue0 di default diverse, e peraltro in numero inferiore, rispetto a quelle dei modelli \ue0 la Altman ed un considerevole abbattimento dell\u2019errore di previsione

Notch signaling sustains the expression of Mcl-1 and the activity of eIF4E to promote cell survival in CLL

In chronic lymphocytic leukemia (CLL), Notch1 and Notch2 signaling is constitutively activated and contributes to apoptosis resistance. We show that genetic inhibition of either Notch1 or Notch2, through small-interfering RNA, increases apoptosis of CLL cells and is associated with decreased levels of the anti-apoptotic protein Mcl-1. Thus, Notch signaling promotes CLL cell survival at least in part by sustaining Mcl-1 expression. In CLL cells, an enhanced Notch activation also contributes to the increase in Mcl-1 expression and cell survival induced by IL-4.Mcl-1 downregulation by Notch targeting is not due to reduced transcription or degradation by caspases, but in part, to increased degradation by the proteasome. Mcl-1 downregulation by Notch targeting is also accompanied by reduced phosphorylation of eukaryotic translation initiation factor 4E (eIF4E), suggesting that this protein is another target of Notch signaling in CLL cells.Overall, we show that Notch signaling sustains CLL cell survival by promoting Mcl-1 expression and eIF4E activity, and given the oncogenic role of these factors, we underscore the therapeutic potential of Notch inhibition in CLL

How can Plan Ceibal Land into the Age of Big Data?

In 2007, Plan Ceibal became the first nationwide ubiquitous educational computer program in the world based on the 1:1 model. It is one of the most important programs implemented by Uruguay’s Government to minimize digital divide and is based upon three pillars: equity, learning and technology. As of 2007, Plan Ceibal has covered all public schools, providing every student and teacher in kindergarten, primary and middle school with a laptop or tablet and internet access in the school. To date, Plan Ceibal has close to 700,000 beneficiaries, each with their own device. Since 2011, the Plan has focused on providing the learning community with a wide range of digital content to enhance the teaching and learning process, most notably Learning Management Systems, Mathematics Adaptive Platform, remote English teaching and an online library. Today, Plan Ceibal operates and integrates a large scale of databases fed by a number of management and educational activities. This abundance of data presents a great challenge and a large opportunity to exploit and transform mass data into rich information. The main goal of this article is to describe the most relevant data sources and present an ongoing data analysis research grounded by a case study. In addition, this paper suggests next steps required to implement a learning analytics strategy within Plan Ceibal. If well exploited, this evidence based data can be used to support and improve the current technology and learning educational policies.IB

5′UTR point substitutions and N-terminal truncating mutations of ANKRD26 in acute myeloid leukemia

Thrombocytopenia 2 (THC2) is an inherited disorder caused by monoallelic single nucleotide substitutions in the 5'UTR of the ANKRD26 gene. Patients have thrombocytopenia and increased risk of myeloid malignancies, in particular, acute myeloid leukemia (AML). Given the association of variants in the ANKRD26 5'UTR with myeloid neoplasms, we investigated whether, and to what extent, mutations in this region contribute to apparently sporadic AML. To this end, we studied 250 consecutive, non-familial, adult AML patients and screened the first exon of ANKRD26 including the 5'UTR. We found variants in four patients. One patient had the c.-125T>G substitution in the 5'UTR, while three patients carried two different variants in the 5' end of the ANKRD26 coding region (c.3G>A or c.105C>G). Review of medical history showed that the patient carrying the c.-125T>G was actually affected by typical but unrecognized THC2, highlighting that some apparently sporadic AML cases represent the evolution of a well-characterized familial predisposition disorder. As regards the c.3G>A and the c.105C>G, we found that both variants result in the synthesis of N-terminal truncated ANKRD26 isoforms, which are stable and functional in cells, in particular, have a strong ability to activate the MAPK/ERK signaling pathway. Moreover, investigation of one patient with the c.3G>A showed that mutation was associated with strong ANKRD26 overexpression in vivo, which is the proposed mechanism for predisposition to AML in THC2 patients. These data provide evidence that N-terminal ANKRD26 truncating mutations play a potential pathogenetic role in AML. Recognition of AML patients with germline ANKRD26 pathogenetic variants is mandatory for selection of donors for bone marrow transplantation

UNet and MobileNet CNN-based model observers for CT protocol optimization: comparative performance evaluation by means of phantom CT images

Purpose: The aim of this work is the development and characterization of a model observer (MO) based on convolutional neural networks (CNNs), trained to mimic human observers in image evaluation in terms of detection and localization of low-contrast objects in CT scans acquired on a reference phantom. The final goal is automatic image quality evaluation and CT protocol optimization to fulfill the ALARA principle. Approach: Preliminary work was carried out to collect localization confidence ratings of human observers for signal presence/absence from a dataset of 30,000 CT images acquired on a PolyMethyl MethAcrylate phantom containing inserts filled with iodinated contrast media at different concentrations. The collected data were used to generate the labels for the training of the artificial neural networks. We developed and compared two CNN architectures based respectively on Unet and MobileNetV2, specifically adapted to achieve the double tasks of classification and localization. The CNN evaluation was performed by computing the area under localization-ROC curve (LAUC) and accuracy metrics on the test dataset. Results: The mean of absolute percentage error between the LAUC of the human observer and MO was found to be below 5% for the most significative test data subsets. An elevated inter-rater agreement was achieved in terms of S-statistics and other common statistical indices. Conclusions: Very good agreement was measured between the human observer and MO, as well as between the performance of the two algorithms. Therefore, this work is highly supportive of the feasibility of employing CNN-MO combined with a specifically designed phantom for CT protocol optimization programs

Presupernova Structure of Massive Stars

Issues concerning the structure and evolution of core collapse progenitor stars are discussed with an emphasis on interior evolution. We describe a program designed to investigate the transport and mixing processes associated with stellar turbulence, arguably the greatest source of uncertainty in progenitor structure, besides mass loss, at the time of core collapse. An effort to use precision observations of stellar parameters to constrain theoretical modeling is also described.Comment: Proceedings for invited talk at High Energy Density Laboratory Astrophysics conference, Caltech, March 2010. Special issue of Astrophysics and Space Science, submitted for peer review: 7 pages, 3 figure

Antiproton slowing Down in H2 and He and evidence of nuclear stopping power

We report stopping powers of hydrogen and helium for antiprotons of kinetic energies ranging from about 0.5 keV to 1.1 MeV. The Barkas effect, i.e., a difference in the stopping power for antiprotons and protons of the same energy in the same material, shows up clearly in either of the gases. Moreover, below ≈0.5 keV there is indirect evidence for an increase of the antiproton stopping power. This "nuclear" effect, i.e., energy losses in quasimolecular interactions, shows up in fair agreement with theoretical predictions

Experimental antiproton nuclear stopping power in H2 and D2

Data about antiprotons slowing down in gaseous targets at very low energies (E<1 keV) show that the stopping power in D2 is lower than in H2; the right way to explain this behavior seems to be through a nuclear stopping power derived from the classical Rutherford formula