The first Frontier Fields cluster: 4.5{\mu}m excess in a z~8 galaxy candidate in Abell 2744

We present in this letter the first analysis of a z~8 galaxy candidate found in the Hubble and Spitzer imaging data of Abell 2744, as part of the Hubble Frontier Fields legacy program. We applied the most commonly-used methods to select exceptionally high-z galaxies by combining non-detection and color-criteria using seven HST bands. We used GALFIT on IRAC images for fitting and subtracting contamination of bright nearby sources. The physical properties have been inferred from SED-fitting using templates with and without nebular emission. This letter is focussed on the brightest candidate we found (m$_{F160W}$=26.2) over the 4.9 arcmin$^2$ field of view covered by the WFC3. It shows a non-detection in the ACS bands and at 3.6{\mu}m whereas it is clearly detected at 4.5{\mu}m with rather similar depths. This break in the IRAC data could be explained by strong [OIII]+H{\beta} lines at z~8 which contribute to the 4.5{\mu}m photometry. The best photo-z is found at z~8.0$^{+0.2}_{-0.5}$, although solutions at low-redshift (z~1.9) cannot be completely excluded, but they are strongly disfavoured by the SED-fitting work. The amplification factor is relatively small at {\mu}=1.49$\pm$0.02. The Star Formation Rate in this object is ranging from 8 to 60 Mo/yr, the stellar mass is in the order of M$_{\star}$=(2.5-10) x 10$^{9}$Mo and the size is r~0.35$\pm$0.15 kpc. This object is one of the first z~8 LBG candidates showing a clear break between 3.6{\mu}m and 4.5{\mu}m which is consistent with the IRAC properties of the first spectroscopically confirmed galaxy at a similar redshift. Due to its brightness, the redshift of this object could potentially be confirmed by near infrared spectroscopy with current 8-10m telescopes. The nature of this candidate will be revealed in the coming months with the arrival of new ACS and Spitzer data, increasing the depth at optical and near-IR wavelengths.Comment: 4 pages, 2 figures, Accepted for publication in Astronomy and Astrophysics Letter

Spider mite (Acari: Tetranychidae) mitochondrial COI phylogeny reviewed: host plant relationships, phylogeography, reproductive parasites and barcoding

The past 15 years have witnessed a number of molecular studies that aimed to resolve issues of species delineation and phylogeny of mites in the family Tetranychidae. The central part of the mitochondrial COI region has frequently been used for investigating intra- and interspecific variation. All these studies combined yield an extensive database of sequence information of the family Tetranychidae. We assembled this information in a single alignment and performed an overall phylogenetic analysis. The resulting phylogeny shows that important patterns have been overlooked in previous studies, whereas others disappear. It also reveals that mistakes were made in submitting the data to GenBank, which further disturbed interpretation of the data. Our total analysis clearly shows three clades that most likely correspond to the species T. urticae, T. kanzawai and T. truncatus. Intraspecific variation is very high, possibly due to selective sweeps caused by reproductive parasites. We found no evidence for host plant associations and phylogeographic patterns in T. urticae are absent. Finally we evaluate the application of DNA barcoding

Modelling wine astringency from its chemical composition using machine learning algorithms

Aims: The present work aims to predict sensory astringency from wine chemical composition using machine learning algorithms. Material and results: Moristel grapes from different vineblocks and at different stages of ripening were collected. Eleven different wines were produced in 75 L tanks in triplicate, and further sensory factors were described by the rate-all-that-apply method with a trained panel of participants. The polyphenolic composition was characterised in wines by measuring the concentration and activity of tannins using UHPLC-UV/VIS, the mean degree of polymerisation (mDP. and the composition of tannins using thiolysis followed by UHPLC-MS. Conventional oenological parameters were analysed using FTIR and UV-Vis. Machine learning was applied to build models for predicting a wines astringency from its chemical composition. The best model was obtained using the support vector regressor (radial kernel) algorithm presenting a root-mean-square error (RMSE) value of 0.190. Conclusions: The main variables of the astringency model were the % of procyanidins constituting tannins and ethanol content, followed by other eight variables related to tannin structure and acidity. Significance of the study: These results increase the knowledge of chemical variables related to the perception of wine astringency and provide tools to control and optimise grape and wine production stages to modulate astringency and maximise quality and the consumer appeal of wines

Actual performance of mechanical ventilators in ICU: a multicentric quality control study.

Even if the performance of a given ventilator has been evaluated in the laboratory under very well controlled conditions, inappropriate maintenance and lack of long-term stability and accuracy of the ventilator sensors may lead to ventilation errors in actual clinical practice. The aim of this study was to evaluate the actual performances of ventilators during clinical routines. A resistance (7.69 cmH(2)O/L/s) - elastance (100 mL/cmH(2)O) test lung equipped with pressure, flow, and oxygen concentration sensors was connected to the Y-piece of all the mechanical ventilators available for patients in four intensive care units (ICUs; n = 66). Ventilators were set to volume-controlled ventilation with tidal volume = 600 mL, respiratory rate = 20 breaths/minute, positive end-expiratory pressure (PEEP) = 8 cmH(2)O, and oxygen fraction = 0.5. The signals from the sensors were recorded to compute the ventilation parameters. The average ± standard deviation and range (min-max) of the ventilatory parameters were the following: inspired tidal volume = 607 ± 36 (530-723) mL, expired tidal volume = 608 ± 36 (530-728) mL, peak pressure = 20.8 ± 2.3 (17.2-25.9) cmH(2)O, respiratory rate = 20.09 ± 0.35 (19.5-21.6) breaths/minute, PEEP = 8.43 ± 0.57 (7.26-10.8) cmH(2)O, oxygen fraction = 0.49 ± 0.014 (0.41-0.53). The more error-prone parameters were the ones related to the measure of flow. In several cases, the actual delivered mechanical ventilation was considerably different from the set one, suggesting the need for improving quality control procedures for these machines