Observation and measurement of Higgs boson decays to WW∗ with the ATLAS detector

We report the observation of Higgs boson decays to WW∗ based on an excess over background of 6.1 standard deviations in the dilepton final state, where the Standard Model expectation is 5.8 standard deviations. Evidence for the vector-boson fusion (VBF) production process is obtained with a significance of 3.2 standard deviations. The results are obtained from a data sample corresponding to an integrated luminosity of 25 pb−1 from s√=7 and 8 TeV pp collisions recorded by the ATLAS detector at the LHC. For a Higgs boson mass of 125.36 GeV, the ratio of the measured value to the expected value of the total production cross section times branching fraction is 1.09+0.16−0.15 (stat.)+0.17−0.14 (syst.). The corresponding ratios for the gluon fusion and vector-boson fusion production mechanisms are 1.02±0.19 (stat.)+0.22−0.18 (syst.) and 1.27+0.44−0.40 (stat.)+0.30−0.21 (syst.), respectively. At s√=8 TeV, the total production cross sections are measured to be σ(gg→ H→WW∗)=4.6±0.9(stat.)+0.8−0.7(syst.)pb and σ(VBF H→WW∗)=0.51+0.17−0.15(stat.)+0.13−0.08(syst.)pb. The fiducial cross section is determined for the gluon-fusion process in exclusive final states with zero or one associated jet.

Sirtuins and proteolytic systems: implications for pathogenesis of synucleinopathies

Insoluble and fibrillar forms of a-synuclein are the major components of Lewy bodies, a hallmark of several sporadic and inherited neurodegenerative diseases known as synucleinopathies. a-Synuclein is a natural unfolded and aggregation-prone protein that can be degraded by the ubiquitin-proteasomal system and the lysosomal degradation pathways. a-Synuclein is a target of the main cellular proteolytic systems, but it is also able to alter their function further, contributing to the progression of neurodegeneration. Aging, a major risk for synucleinopathies, is associated with a decrease activity of the proteolytic systems, further aggravating this toxic looping cycle. Here, the current literature on the basic aspects of the routes for a-synuclein clearance, as well as the consequences of the proteolytic systems collapse, will be discussed. Finally, particular focus will be given to the sirtuins's role on proteostasis regulation, since their modulation emerged as a promising therapeutic strategy to rescue cells from a-synuclein toxicity. The controversial reports on the potential role of sirtuins in the degradation of a-synuclein will be discussed. Connection between sirtuins and proteolytic systems is definitely worth of further studies to increase the knowledge that will allow its proper exploration as new avenue to fight synucleinopathies.

The Portuguese version of the Psychological Adjustment to Separation Test-Part A (PAST-A): a study with recently and non-recently divorced adults

Past research has demonstrated that divorced adults show more health problems and psychological distress than married adults. Considering the high prevalence rates of divorce among Western countries, new and robust measures should be developed to measure psychological distress after this specific transition in adulthood. The aim of this study was to adapt and validate a Portuguese version of the Psychological Adjustment to Separation Test-Part A (PAST-A; Sweeper and Halford in J Family Psychol 20(4):632–640, 2006). PAST-A is a self-report measure that assesses two key dimensions of separation adjustment problems: lonely-negativity and former partner attachment. Psychometric properties of the Portuguese version of PAST-A were assessed in terms of factor structure, internal consistency, and convergent and divergent validity, in an online convenience sample with divorced adults (N = 460). The PAST-A two-factor structure was confirmed by exploratory and confirmatory factor analyses, with each factor demonstrating very satisfactory internal consistency and good convergence. In terms of discriminant validity, the Portuguese PAST-A reveals a distinct factor from psychological growth after divorce. The results provided support for the use of the Portuguese PAST-A with divorced adults and also suggested that the explicative factors of the psychological adjustment to divorce may be cross-cultural stable. The non-existence of validated divorce-related well-being measures and its implications for divorce research are also discussed.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Concrete with triphasic conductive materials for self-monitoring of cracking development subjected to flexure

In this study, the macro steel fiber (SF), carbon fiber (CF) and nano carbon black (NCB) as triphasic conductive materials were added into concrete, in order to improve the conductivity and ductility of concrete. The influence of NCB, SF and CF on the post crack behavior and conductivity of concrete was explored. The effect of the triphasic conductive materials on the self-diagnosing ability to the load–deflection property and crack widening of conductive concrete member subjected to bending were investigated. The relationship between the fractional change in surface impedance (FCR) and the crack opening displacement (COD) of concrete beams with conductive materials has been established. The results illustrated that there is a linear relationship between COD and FCR.

The Data Mining applied for the prediction of highway roughness due to overloaded trucks

Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks

The use of canonical correlation analysis to assess the relationship between executive functioning and verbal memory in older adults

Executive functioning (EF), which is considered to govern complex cognition, and verbal memory (VM) are constructs assumed to be related. However, it is not known the magnitude of the association between EF and VM, and how sociodemographic and psychological factors may affect this relationship, including in normal aging. In this study, we assessed different EF and VM parameters, via a battery of neurocognitive/psychological tests, and performed a Canonical Correlation Analysis (CCA) to explore the connection between these constructs, in a sample of middle- aged and older healthy individuals without cognitive impairment (N = 563, 50+ years of age). The analysis revealed a positive and moderate association between EF and VM independently of gender, age, education, global cognitive performance level, and mood. These results confirm that EF presents a significant association with VM performance.