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I use matched employer-employee records merged with corporate tax information from 2003 to 2017 to estimate labor market-wide effects of mergers and acquisitions in Brazil. Labor markets are defined by pairs of commuting zone and industry sector. In the following year of a merger, market size falls by 10.8%. The employment adjustment is concentrated in merging firms. For the firms not involved in M&As, I estimate a 1.07% decline in workers earnings and a positive, although not significant, increase in their size. Most mergers have a predicted impact of zero points in concentration, measured by the Herfindahl-Hirschman Index (HHI). I spillover firms, earnings decline similarly for mergers with high and low predicted changes in HHI. Contrary to the recent literature on market concentration in developed economies, I find no evidence of oligopsonistic behavior in Brazilian labor markets.

Maximal lactate steady state (MLSS) is defined as the highest intensity of exercise where the balance between the production and removal of lactate in the blood occurs. The MLSS deermination is based on several tests carried out on different days using invasive procedures (i.e. blood sampling), making its use limited in practical situations. Therefore, since the 1980s, several other methods/protocols non-invasive and/or less time-consuming have been used to estimate the MLSS. In addition, much debate in the literature has been done about the validity of estimating MLSS by these methods/tests. Because of the large number of studies and methods/tests and the great variability among them, several meta-analyses will be carried out separately for each type of predictor, as follow: 1- Maximal lactate steady state versus lactate thresholds methods; 2- Maximal lactate steady state versus non-invasive thresholds methods from incremental exercise testing; 3- Maximal lactate steady state versus lactate minimum testing; and 4- Maximal lactate steady state versus performance measures, including time-trials and critical power calculations.

Warming up is a procedure widely used by athletes during training and competition. Its goal is to prepare the athlete for the subsequent task, prevent lesions, and increase the performance during competition. Although empirical observations claim that warming up has an ergogenic effect, the scientific data are still controversial, with studies showing positive, negative, and no influence of the warm-up over the performance. However, there is no consensus regarding the ideal warm-up format (e.g. volume, intensity, time between warm-up and performance), which may explain the discrepancy between the studies. Therefore, the present project aims to investigate the effect of warm-up protocols in the performance of cyclic sports (e.g. cycling, running, swimming, kayaking, skiing).

Despite being initially considered at higher risk for severe COVID-19, sickle cell disease (SCD) patients have mostly presented clinical severity similar to the general population. As their vulnerability to become infected remains uncertain, we assessed the seroreactivity for SARS-CoV-2 to estimate the prevalence of infection and possible phenotypic and socioeconomic determinants for their contagion. Serologic evaluation was performed on 135 patients with an overall prevalence of 11%; positivity was associated with older age and use of public transportation. We speculate that social distancing instructions recommended by our clinic may have contributed to lower levels of infection, but potential protection factors need further investigation FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP Aberto

International audience; Acute lymphoblastic leukemia of T cell lineage (T-ALL) is an aggressive ă malignant disease which accounts for 15 % of childhood ALL. T(11;14) is ă the more frequent chromosomal abnormality in childhood T-ALL, but its ă prognostic value remained controversial. Our aim was to analyze the ă outcome of childhood T-ALL with t(11;14) to know if the presence of this ă translocation is associated with a poor prognosis. We conducted a ă retrospective study from a series of 20 patients with t(11;14), treated ă in two consecutive trials from the European Organization for Research ă and Treatment of Cancer Children Leukemia Group over a 19-year period ă from 1989 to 2008. There were no significant differences between the 2 ă consecutive groups of patients with t(11;14) regarding the clinical and ă biological features at diagnosis. Among 19 patients who reached complete ă remission, 9 patients relapsed. We noticed 7 deaths all relapse- or ă failure-related. In the 58881 study, a presence of t(11;14) was ă associated with a poor outcome with an event-free survival at 5 years at ă 22.2 % versus 65.1 % for the non-t(11;14) T-ALL (p = 0.0004). In the ă more recent protocol, the outcome of T-ALL with t(11;14) reached that of ă non-t(11;14) T-ALL with an event-free survival at 5 years at 65.5 versus ă 74.9 % (p = 0.93). The presence of t(11;14) appeared as a poor ă prognostic feature in the 58881 trial whereas this abnormality no longer ă affected the outcome in the 58951 study. This difference is probably ă explained by the more intensive chemotherapy in the latest trial.

Motivation: Profile hidden Markov Models (pHMMs) are a popular and very useful tool in the detection of the remote homologue protein families. Unfortunately, their performance is not always satisfactory when proteins are in the 'twilight zone'. We present HMMER-STRUCT, a model construction algorithm and tool that tries to improve pHMM performance by using structural information while training pHMMs. As a first step, HMMER-STRUCT constructs a set of pHMMs. Each pHMM is constructed by weighting each residue in an aligned protein according to a specific structural property of the residue. Properties used were primary, secondary and tertiary structures, accessibility and packing. HMMER-STRUCT then prioritizes the results by voting. Results: We used the SCOP database to perform our experiments. Throughout, we apply leave-one-family-out cross-validation over protein superfamilies. First, we used the MAMMOTH-mult structural aligner to align the training set proteins. Then, we performed two sets of experiments. In a first experiment, we compared structure weighted models against standard pHMMs and against each other. In a second experiment, we compared the voting model against individual pHMMs. We compare method performance through ROC curves and through Precision/Recall curves, and assess significance through the paired two tailed t-test. Our results show significant performance improvements of all structurally weighted models over default HMMER, and a significant improvement in sensitivity of the combined models over both the original model and the structurally weighted models., Comment: 6 pages, 7 figures

Several techniques for implementing Prolog in a efficient manner have been devised since the original interpreter, many of them aimed at achieving more speed. There are two main approaches to efficient Prolog implementation: (1) compilers to bytecode and then interpreting it (emulators) or (2) compilers to native code. Emulators have smaller load/compilation time and are a good solution for their simplicity when speed is not a priority. Compilers are more complex than emulators, and the difference is much more acute if some form of code analysis is performed as part of the compilation, which impacts development time. Generation of low level code promises faster programs at the expense of using more resources during the compilation phase. In our work besides using an mixed execution mode, we design an optimizing compiler that using type feedback profiling, dynamic compilation and dynamic deoptimization for improving the performance of logic programming languages.

The present study intended to investigate whether there was a relationship between pregnant women with dyspeptic complaints and with and without Helicobacter pylori infection in the first and second trimester of pregnancy.H.pylori seropositivity was determined in 146 (aged 20 to 40 years) antenatal patients at 10 to 23 weeks gestation. H.Pylori ser um Immunoglobulin IgG antibody concentrations were determined by enzyme-linked immunoadsorbent assay (ELISA). Age, obesity, parity, outcome of pregnancy and dyspeptic symptoms were registered in the patients' medical records. Chi-square and Student's t-test were used for statistical analysis of the data.Prevalence of H.pylori infection in the population studied was 65.7% (96 of 146). H pylori infection was not associated with parity, preterm delivery, obesity or with dyspeptic symptoms. Women positive for H.pylori IgG were older (30, 45 +/- 6.87 vs 27, 96 +/- 7.44, p= 0.045565*) than H. pylori negative patients.In this study there was no association of H pylori infection with dyspeptic symptoms in pregnancy, nor was its presence associated with obesity, parity and outcome of pregnancy.

Granularity control is a method to improve parallel execution performance by limiting excessive parallelism. The general idea is that if the gain obtained by executing a task in parallel is less than the overheads required to support parallel execution, then the task is better executed sequentially. Traditionally, in logic programming, task size is estimated as the sequential time-complexity of evaluating the task. Tasks are only executed in parallel if task size exceeds a pre-determined threshold. We argue in this paper that the estimation of task complexity, on its own, is not an ideal metric for improving the performance of parallel programs through granularity control. We present a new metric for measuring granularity, based on a notion of emphdistance. We present some initial results with two very simple methods of using this metric for granularity control. We then discuss how more sophisticated granularity control methods can be devised using the new metric.

We present CLP(BN), a novel approach that aims at expressing Bayesian networks through the constraint logic programming framework. Arguably, an important limitation of traditional Bayesian networks is that they are propositional, and thus cannot represent relations between multiple similar objects in multiple contexts. Several researchers have thus proposed first-order languages to describe such networks. Namely, one very successful example of this approach are the Probabilistic Relational Models (PRMs), that combine Bayesian networks with relational database technology. The key difficulty that we had to address when designing CLP(cal{BN}) is that logic based representations use ground terms to denote objects. With probabilitic data, we need to be able to uniquely represent an object whose value we are not sure about. We use {sl Skolem functions} as unique new symbols that uniquely represent objects with unknown value. The semantics of CLP(cal{BN}) programs then naturally follow from the general framework of constraint logic programming, as applied to a specific domain where we have probabilistic data. This paper introduces and defines CLP(cal{BN}), and it describes an implementation and initial experiments. The paper also shows how CLP(cal{BN}) relates to Probabilistic Relational Models (PRMs), Ngo and Haddawys Probabilistic Logic Programs, AND Kersting AND De Raedts Bayesian Logic Programs., Appears in Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI2003)

Insulin resistance is a major predictor of the development of metabolic disorders. Sirtuins (SIRTs) have emerged as potential targets that can be manipulated to counteract age-related diseases, including type 2 diabetes. SIRT2 has been recently shown to exert important metabolic effects, but whether SIRT2 regulates insulin sensitivity in hepatocytes is currently unknown. The aim of this study is to investigate this possibility and to elucidate underlying molecular mechanisms. Here, we show that SIRT2 is downregulated in insulin-resistant hepatocytes and livers, and this was accompanied by increased generation of reactive oxygen species, activation of stress-sensitive ERK1/2 kinase, and mitochondrial dysfunction. Conversely, SIRT2 overexpression in insulin-resistant hepatocytes improved insulin sensitivity, mitigated reactive oxygen species production and ameliorated mitochondrial dysfunction. Further analysis revealed a reestablishment of mitochondrial morphology, with a higher number of elongated mitochondria rather than fragmented mitochondria instigated by insulin resistance. Mechanistically, SIRT2 was able to increase fusion-related protein Mfn2 and decrease mitochondrial-associated Drp1. SIRT2 also attenuated the downregulation of TFAM, a key mtDNA-associated protein, contributing to the increase in mitochondrial mass. Importantly, we found that SIRT2 expression in PBMCs of human subjects was negatively correlated with obesity and insulin resistance. These results suggest a novel function for hepatic SIRT2 in the regulation of insulin sensitivity and raise the possibility that SIRT2 activators may offer novel opportunities for preventing or treating insulin resistance and type 2 diabetes.

Precision-recall (PR) curves and the areas under them are widely used to summarize machine learning results, especially for data sets exhibiting class skew. They are often used analogously to ROC curves and the area under ROC curves. It is known that PR curves vary as class skew changes. What was not recognized before this paper is that there is a region of PR space that is completely unachievable, and the size of this region depends only on the skew. This paper precisely characterizes the size of that region and discusses its implications for empirical evaluation methodology in machine learning., Comment: ICML2012, fixed citations to use correct tech report number