"Google it!" Forecasting the US unemployment rate with a Google job search index

In this paper we suggest the use of an internet job-search indicator (Google Index, GI) as the best leading indicator to predict the US unemployment rate. We perform a deep out-of-sample comparison of many forecasting models. With respect to the previous literature we concentrate on the monthly series extending the out-of-sample forecast comparison with models that adopt both our preferred leading indicator (GI), the more standard initial claims or combinations of both. Our results show that the GI indeed helps in predicting the US unemployment rate even after controlling for the effects of data snooping. Robustness checks show that models augmented with the GI perform better than traditional ones even in most state-level forecasts and in comparison with the Survey of Professional Forecasters' federal level predictions

Methylated HBHA Produced in M. smegmatis Discriminates between Active and Non-Active Tuberculosis Disease among RD1-Responders

A challenge in tuberculosis (TB) research is to develop a new immunological test that can help distinguish, among subjects responsive to QuantiFERON TB Gold In tube (QFT-IT), those who are able to control Mtb replication (remote LTBI, recent infection and past TB) from those who cannot (active TB disease). IFN-\u3b3 response to the Heparin-binding-hemagglutinin (HBHA) of Mtb has been associated with LTBI, but the cumbersome procedures of purifying the methylated and immunological active form of the protein from Mtb or M. bovis Bacillus Calmette et Guerin (BCG) have prevented its implementation in a diagnostic test. Therefore, the aim of the present study was to evaluate the IFN-\u3b3 response to methylated HBHA of Mtb produced in M. smegmatis (rHBHAms) in individuals at different stages of TB who scored positive to QFT-IT

Overweight/Obesity and Respiratory and Allergic Disease in Children: International Study of Asthma and Allergies in Childhood (ISAAC) Phase Two

BackgroundChildhood obesity and asthma are increasing worldwide. A possible link between the two conditions has been postulated.MethodsCross-sectional studies of stratified random samples of 8–12-year-old children (n = 10 652) (16 centres in affluent and 8 centres in non-affluent countries) used the standardized methodology of ISAAC Phase Two. Respiratory and allergic symptoms were ascertained by parental questionnaires. Tests for allergic disease were performed. Height and weight were measured, and overweight and obesity were defined according to international definitions. Prevalence rates and prevalence odds ratios were calculated.ResultsOverweight (odds ratio = 1.14, 95%-confidence interval: 0.98; 1.33) and obesity (odds ratio = 1.67, 95%-confidence interval: 1.25; 2.21) were related to wheeze. The relationship was stronger in affluent than in non-affluent centres. Similar results were found for cough and phlegm, rhinitis and eczema but the associations were mostly driven by children with wheeze. There was a clear association of overweight and obesity with airways obstruction (change in FEV1/FVC, −0.90, 95%-confidence interval: −1.33%; −0.47%, for overweight and −2.46%, 95%-confidence interval: −3.84%; −1.07%, for obesity) whereas the results for the other objective markers, including atopy, were null.ConclusionsOur data from a large international child population confirm that there is a strong relation of body mass index with wheeze especially in affluent countries. Moreover, body mass index is associated with an objective marker of airways obstruction (FEV1/FVC) but no other objective markers of respiratory and allergic disorders

CYP17, GSTP1, PON1 and GLO1 gene polymorphisms as risk factors for breast cancer: an Italian case-control study

<p>Abstract</p> <p>Background</p> <p>Estrogens, environmental chemicals with carcinogenic potential, as well as oxidative and carbonyl stresses play a very important role in breast cancer (BC) genesis and progression. Therefore, polymorphisms of genes encoding enzymes involved in estrogen biosynthesis pathway and in the metabolic activation of pro-carcinogens to genotoxic intermediates, such as cytochrome P450C17α (CYP17), endogenous free-radical scavenging systems, such as glutathione S-transferase (GSTP1) and paraoxonase 1 (PON1), and anti-glycation defenses, such as glyoxalase I (GLO1), could influence individual susceptibility to BC. In the present case-control study, we investigated the possible association of CYP17 A1A2, GSTP1 ILE105VAL, PON1 Q192R or L55M, and GLO1 A111E polymorphisms with the risk of BC.</p> <p>Methods</p> <p>The above-said five polymorphisms were characterized in 547 patients with BC and in 544 healthy controls by PCR/RFLP methods, using DNA from whole blood. To estimate the relative risks, Odds ratios and 95% confidence intervals were calculated using unconditional logistic regression after adjusting for the known risk factors for BC.</p> <p>Results</p> <p>CYP17 polymorphism had no major effect in BC proneness in the overall population. However, it modified the risk of BC for certain subgroups of patients. In particular, among premenopausal women with the A1A1 genotype, a protective effect of later age at menarche and parity was observed. As to GSTP1 and PON1 192 polymorphisms, the mutant Val and R alleles, respectively, were associated with a decreased risk of developing BC, while polymorphisms in PON1 55 and GLO1 were associated with an increased risk of this neoplasia. However, these findings, while nominally significant, did not withstand correction for multiple testing.</p> <p>Conclusion</p> <p>Genetic polymorphisms in biotransformation enzymes CYP17, GSTP1, PON1 and GLO1 could be associated with the risk for BC. Although significances did not withstand correction for multiple testing, the results of our exploratory analysis warrant further studies on the above mentioned genes and BC.</p

"Google it!" Forecasting the US unemployment rate with a Google job search index

In this paper we suggest the use of an internet job-search indicator (Google Index, GI) as the best leading indicator to predict the US unemployment rate. We perform a deep out-of-sample comparison of many forecasting models. With respect to the previous literature we concentrate on the monthly series extending the out-of-sample forecast comparison with models that adopt both our preferred leading indicator (GI), the more standard initial claims or combinations of both. Our results show that the GI indeed helps in predicting the US unemployment rate even after controlling for the effects of data snooping. Robustness checks show that models augmented with the GI perform better than traditional ones even in most state-level forecasts and in comparison with the Survey of Professional Forecasters' federal level predictions.Google econometrics, Forecast comparison, Keyword search, US unemployment, Time series models.