Evaluating currency crises: A multivariate Markov regime switching approach

This paper provides an empirical framework to analyse the nature of currency crises byextending earlier work of Jeanne and Masson (2000) who suggest that a currency crisismodel with multiple equilibria can be estimated using Markov regime switching (MRS)models. However, Jeanne and Masson (2000) assume that the transition probabilitiesacross equilibria are constant and independent of fundamentals. Thus, currency crisis isdriven by a sunspot unrelated to fundamentals. This paper further contributes to theliterature by suggesting a multivariate MRS model to analyse the nature of currencycrises. In the new set up, one can test for the impact of the unobserved dynamics offundamentals on the probability of devaluation. Empirical evidence shows thatexpectations about fundamentals, which are reflected by their unobserved state variables,not only affect the probability of devaluation but also can be used to forecast a currencycrisis one period ahead

Comparative Proteomic Profiling of Methicillin‐Susceptible and Resistant Staphylococcus aureus

Purpose Staphylococcus aureus is a highly successful human pathogen responsible for wide range of infections. In this study, we provide insights into the virulence, pathogenicity, and antimicrobial resistance determinants of methicillin susceptible and methicillin resistant Staphylococcus aureus (MSSA; MRSA) recovered from non‐healthcare environments. Experiment design Three environmental MSSA and three environmental MRSA were selected for proteomic profiling using iTRAQ MS/MS. Gene Ontology (GO) Annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Annotation were applied to interpret the functions of the proteins detected. Results 792 proteins were identified in MSSA and MRSA. Comparative analysis of MRSA and MSSA revealed that 8 of out 792 proteins were up‐regulated and 156 were down‐regulated. Proteins that had differences in abundance were predominantly involved in catalytic and binding activity. Among 164 differently abundant proteins, 29 were involved in pathogenesis, antimicrobial resistance, stress response, mismatch repair and cell wall synthesis. Twenty‐two proteins associated with pathogenicity, including spa, sbi, clfA and dlt were up‐regulated in MRSA. Moreover, the up‐regulated pathogenic protein entC2 in MSSA was determined to be a super antigen potentially capable of triggering toxic shock syndrome in the host. Conclusions Enhanced pathogenicity, antimicrobial resistance and stress response were observed in MRSA compared to MSSA

Generalised Pustular Psoriasis (von Zumbusch type) following renal Transplantation. Report of a case and review of the literature

Generalized pustular psoriasis appears as an uncommon variant form of psoriasis consisting of widespread pustules on an erythematous background (von Zumbusch). A 39-year old male patient with a history of plaque psoriasis since the age of 9 who had an acute onset of generalized pustular psoriasis 12 days after he underwent renal transplantation is presented. Despite administered immunosuppression for transplantation the addition of cyclosporine A and methotrexate did not reverse the ongoing process of disease and the patient died on the 57th post-transplant day due to multiorgan failure following severe bone marrow suppression

Hyperactivation of Nrf2 increases stress tolerance at the cost of aging acceleration due to metabolic deregulation.

Metazoans viability depends on their ability to regulate metabolic processes and also to respond to harmful challenges by mounting anti-stress responses; these adaptations were fundamental forces during evolution. Central to anti-stress responses are a number of short-lived transcription factors that by functioning as stress sensors mobilize genomic responses aiming to eliminate stressors. We show here that increased expression of nuclear factor erythroid 2-related factor (Nrf2) in Drosophila activated cytoprotective modules and enhanced stress tolerance. However, while mild Nrf2 activation extended lifespan, high Nrf2 expression levels resulted in developmental lethality or, after inducible activation in adult flies, in altered mitochondrial bioenergetics, the appearance of Diabetes Type 1 hallmarks and aging acceleration. Genetic or dietary suppression of Insulin/IGF-like signaling (IIS) titrated Nrf2 activity to lower levels, largely normalized metabolic pathways signaling, and extended flies' lifespan. Thus, prolonged stress signaling by otherwise cytoprotective short-lived stress sensors perturbs IIS resulting in re-allocation of resources from growth and longevity to somatic preservation and stress tolerance. These findings provide a reasonable explanation of why most (if not all) cytoprotective stress sensors are short-lived proteins, and it also explains the build-in negative feedback loops (shown here for Nrf2); the low basal levels of these proteins, and why their suppressors were favored by evolution

Chronic p53-independent p21 expression causes genomic instability by deregulating replication licensing

The cyclin-dependent kinase inhibitor p21WAF1/CIP1 (p21) is a cell-cycle checkpoint effector and inducer of senescence, regulated by p53. Yet, evidence suggests that p21 could also be oncogenic, through a mechanism that has so far remained obscure. We report that a subset of atypical cancerous cells strongly expressing p21 showed proliferation features. This occurred predominantly in p53-mutant human cancers, suggesting p53-independent upregulation of p21 selectively in more aggressive tumour cells. Multifaceted phenotypic and genomic analyses of p21-inducible, p53-null, cancerous and near-normal cellular models showed that after an initial senescence-like phase, a subpopulation of p21-expressing proliferating cells emerged, featuring increased genomic instability, aggressiveness and chemoresistance. Mechanistically, sustained p21 accumulation inhibited mainly the CRL4–CDT2 ubiquitin ligase, leading to deregulated origin licensing and replication stress. Collectively, our data reveal the tumour-promoting ability of p21 through deregulation of DNA replication licensing machinery—an unorthodox role to be considered in cancer treatment, since p21 responds to various stimuli including some chemotherapy drugs

Nonstationary-Volatility Robust Panel Unit Root Tests and the Great Moderation

This paper argues that typical applications of panel unit root tests should take possible nonstationarity in the volatility process of the innovations of the panel time series into account. Nonstationarity volatility arises for instance when there are structural breaks in the innovation variances. A prominent example is the reduction in GDP growth variances enjoyed by many industrialized countries, known as the 'Great Moderation'. It also proposes a new testing approach for panel unit roots that is, unlike many previously suggested tests, robust to such volatility processes. The panel test is based on Simes' (1986) classical multiple test, which combines evidence from time series unit root tests of the series in the panel. As time series unit root tests, we employ recently proposed tests of Cavaliere and Taylor (2008b). The panel test is robust to general patterns of cross-sectional dependence and yet is straightforward to implement, only requiring valid p-values of time series unit root tests, and no resampling. Monte Carlo experiments show that other panel unit root tests suffer from sometimes severe size distortions in the presence of nonstationary volatility, and that this defect can be remedied using the test proposed here. We use the methods developed here to test for unit roots in OECD panels of gross domestic products and infl ation rates, yielding inference robust to the 'Great Moderation'. We fi nd little evidence of trend stationarity, and mixed evidence regarding inflation stationarity.Die vorliegende Arbeit argumentiert, dass typische Anwendungen von Panel-Einheitswurzeltests die Möglichkeit von Nicht-Stationarität in dem Volatilitätsprozess der Innovationen der Panel-Zeitreihen berücksichtigen sollten. Nicht-stationäre Volatilität entsteht z.B. durch Strukturbrüche in den Varianzen der Innovationen. Ein prominentes Beispiel hierfür ist die Verringerung der Varianzen des BIP-Wachstums vieler Industrieländern, welche unter dem Begriff 'Great Moderation' bekannt ist. Außerdem schlägt die Arbeit einen neuen Testansatz für Panel-Einheitswurzeln vor, der im Gegensatz zu vielen zuvor vorgeschlagen Test robust ist gegenüber solchen Volatilitätsprozessen. Der Panel-Test basiert auf Simes' (1986) klassischem multiplen Testverfahren, welches auf einer Kombination von Zeitreihen-Einheitswurzeltests basiert. Als Zeitreihen-Einheitswurzeltest werden hier die kürzlich vorgeschlagenen Tests von Cavaliere und Taylor (2008b) verwendet. Der Panel-Test ist ebenfalls robust gegenüber allgemeinen Querschnittsabhängigkeitsstrukturen und ist einfach zu implementieren, da lediglich gültig p-Werte von Zeitreihen-Einheitswurzeltests erforderlich sind. Monte Carlo Experimente zeigen, dass andere Panel-Einheitswurzeltests bei Präsenz von nicht-stationärer Volatilität häufig unter starken Verzerrungen leiden, und dass dieser Mangel mit Hilfe des hier vorgeschlagen Verfahrens behoben werden kann. Diese Methode wird hier genutzt, um auf das Vorliegen von Einheitswurzeln in Panels der Bruttoinlandsprodukte und der Inflationsraten von OECD-Ländern zu testen. Dabei werden kaum Anzeichen für Trendstationarität und gemischte Evidenz für Inflations-Stationarität gefunden

Cdc6: A multi-functional molecular switch with critical role in carcinogenesis

Research in the last decade revealed an additional role for the Replication Licensing Factor Cdc6 in transcriptional regulation. This novel function has been linked to human cancer development. Here, we summarize all the findings arguing over a role of Cdc6 as a transcriptional repressor and shed light toward new research directions for this field. © 2012 Landes Bioscience

Deep learning: shaping the medicine of tomorrow

Predicting response to therapy is a major challenge in medicine. Machine learning algorithms are promising tools for assisting this aim. Amongst them, Deep Neural Networks are emerging as the most capable of interrogating across multiple data types. Their further development will lead to sophisticated knowledge extraction, shaping the medicine of tomorrow. © 2020, © 2020 Taylor & Francis Group, LLC