Chemoenzymatic synthesis in flow reactors: a rapid and convenient preparation of Captopril

The chemoenzymatic flow synthesis of enantiomerically pure captopril, a widely used antihypertensive drug, is accomplished starting from simple, inexpensive, and readily available reagents. The first step is a heterogeneous biocatalyzed regio- and stereoselective oxidation of cheap prochiral 2-methyl-1,3-propandiol, performed in flow using immobilized whole cells of Acetobacter aceti MIM 2000/28, thus avoiding the use of aggressive and environmentally harmful chemical oxidants. The isolation of the highly hydrophilic intermediate (R)-3-hydroxy-2-methylpropanoic acid is achieved in-line by using a catch and-release strategy. Then, three sequential high-throughput chemical steps lead to the isolation of captopril in only 75 min. In-line quenching and liquid-liquid separation enable breaks in the workflow and other manipulations to be avoided

Search for jet extinction in the inclusive jet-pT spectrum from proton-proton collisions at s=8 TeV

Published by the American Physical Society under the terms of the Creative Commons Attribution 3.0 License. Further distribution of this work must maintain attribution to the author(s) and the published articles title, journal citation, and DOI.The first search at the LHC for the extinction of QCD jet production is presented, using data collected with the CMS detector corresponding to an integrated luminosity of 10.7  fb−1 of proton-proton collisions at a center-of-mass energy of 8 TeV. The extinction model studied in this analysis is motivated by the search for signatures of strong gravity at the TeV scale (terascale gravity) and assumes the existence of string couplings in the strong-coupling limit. In this limit, the string model predicts the suppression of all high-transverse-momentum standard model processes, including jet production, beyond a certain energy scale. To test this prediction, the measured transverse-momentum spectrum is compared to the theoretical prediction of the standard model. No significant deficit of events is found at high transverse momentum. A 95% confidence level lower limit of 3.3 TeV is set on the extinction mass scale

Searches for electroweak neutralino and chargino production in channels with Higgs, Z, and W bosons in pp collisions at 8 TeV

Searches for supersymmetry (SUSY) are presented based on the electroweak pair production of neutralinos and charginos, leading to decay channels with Higgs, Z, and W bosons and undetected lightest SUSY particles (LSPs). The data sample corresponds to an integrated luminosity of about 19.5 fb(-1) of proton-proton collisions at a center-of-mass energy of 8 TeV collected in 2012 with the CMS detector at the LHC. The main emphasis is neutralino pair production in which each neutralino decays either to a Higgs boson (h) and an LSP or to a Z boson and an LSP, leading to hh, hZ, and ZZ states with missing transverse energy (E-T(miss)). A second aspect is chargino-neutralino pair production, leading to hW states with E-T(miss). The decays of a Higgs boson to a bottom-quark pair, to a photon pair, and to final states with leptons are considered in conjunction with hadronic and leptonic decay modes of the Z and W bosons. No evidence is found for supersymmetric particles, and 95% confidence level upper limits are evaluated for the respective pair production cross sections and for neutralino and chargino mass values

Encoding and decoding models in cognitive electrophysiology

Contains fulltext : 179395.pdf (publisher's version ) (Open Access)Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of "Encoding" models, in which stimulus features are used to model brain activity, and "Decoding" models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aim is to provide a practical understanding of predictive modeling of human brain data and to propose best-practices in conducting these analyses.24 p

Antiepileptic drug discontinuation by people with epilepsy in the general population

Objective: Rate, reasons, and predictors of antiepileptic drug (AED) discontinuation were investigated in a well-defined cohort of people with epilepsy to verify efficacy and tolerability of treatment up to 20 years from treatment initiation. Methods: The history of AED usage in children and adults with epilepsy registered with 123 family physicians in an area of Northern Italy between 2000 and 2008 was recorded. Cumulative probabilities of AED withdrawal for specific reasons were estimated using cumulative incidence functions. The probabilities of withdrawing for terminal remission, and of achieving sustained remission while still on treatment, were also evaluated. The roles of sex, age at diagnosis, seizure types, duration at diagnosis, and syndrome were assessed with hazard ratios and 95% confidence intervals. Results: Seven hundred thirty-one of 747 individuals were treated with one or more AEDs during the disease course. The three commonest drugs were valproate, carbamazepine, and phenobarbital. Reported reasons for AED withdrawal were, in decreasing order, terminal remission, ineffectiveness, and adverse events. The probability of withdrawing the first AED for terminal remission was 1.0% at 1 year and increased to 20.0% at 20 years. Corresponding rates were 2.9% and 12.6% for ineffectiveness and 0.5% and 3.3% for adverse events. Reasons for withdrawal varied with individuals' age, sex, disease characteristics, and drugs. Significance: The initial AED given was retained in the majority of cases. Terminal remission, lack of efficacy, and adverse effects were, in decreasing order, the commonest reasons for AED discontinuation. Withdrawal could be predicted by age at diagnosis, sex, and clinical characteristics and varies among drugs