Defining the core proteome of the chloroplast envelope membranes

High-throughput protein localization studies require multiple strategies. Mass spectrometric analysis of defined cellular fractions is one of the complementary approaches to a diverse array of cell biological methods. In recent years, the protein content of different cellular (sub-)compartments was approached. Despite of all the efforts made, the analysis of membrane fractions remains difficult, in that the dissection of the proteomes of the envelope membranes of chloroplasts or mitochondria is often not reliable because sample purity is not always warranted. Moreover, proteomic studies are often restricted to single (model) species, and therefore limited in respect to differential individual evolution. In this study we analyzed the chloroplast envelope proteomes of different plant species, namely, the individual proteomes of inner and outer envelope (OE) membrane of Pisum sativum and the mixed envelope proteomes of Arabidopsis thaliana and Medicago sativa. The analysis of all three species yielded 341 identified proteins in total, 247 of them being unique. 39 proteins were genuine envelope proteins found in at least two species. Based on this and previous envelope studies we defined the core envelope proteome of chloroplasts. Comparing the general overlap of the available six independent studies (including ours) revealed only a number of 27 envelope proteins. Depending on the stringency of applied selection criteria we found 231 envelope proteins, while less stringent criteria increases this number to 649 putative envelope proteins. Based on the latter we provide a map of the outer and inner envelope core proteome, which includes many yet uncharacterized proteins predicted to be involved in transport, signaling, and response. Furthermore, a foundation for the functional characterization of yet unidentified functions of the inner and OE for further analyses is provided

Domain Adaptation with Joint Learning for Generic, Optical Car Part Recognition and Detection Systems (Go-CaRD)

Systems for the automatic recognition and detection of automotive parts are crucial in several emerging research areas in the development of intelligent vehicles. They enable, for example, the detection and modelling of interactions between human and the vehicle. In this paper, we quantitatively and qualitatively explore the efficacy of deep learning architectures for the classification and localisation of 29 interior and exterior vehicle regions on three novel datasets. Furthermore, we experiment with joint and transfer learning approaches across datasets and point out potential applications of our systems. Our best network architecture achieves an F1 score of 93.67 % for recognition, while our best localisation approach utilising state-of-the-art backbone networks achieve a mAP of 63.01 % for detection. The MuSe-CAR-Part dataset, which is based on a large variety of human-car interactions in videos, the weights of the best models, and the code is publicly available to academic parties for benchmarking and future research.Comment: Demonstration and instructions to obtain data and models: https://github.com/lstappen/GoCar

A Randomized, Placebo-controlled Trial of Preemptive Antifungal Therapy for the Prevention of Invasive Candidiasis Following Gastrointestinal Surgery for Intra-abdominal Infections

Patients undergoing emergency gastrointestinal surgery for intra-abdominal infection are at high risk for invasive candidiasis. This exploratory clinical trial could not provide evidence that a preemptive antifungal treatment strategy was effective in this patient grou

A flexible integrative approach based on random forest improves prediction of transcription factor binding sites

Transcription factor binding sites (TFBSs) are DNA sequences of 6-15 base pairs. Interaction of these TFBSs with transcription factors (TFs) is largely responsible for most spatiotemporal gene expression patterns. Here, we evaluate to what extent sequence-based prediction of TFBSs can be improved by taking into account the positional dependencies of nucleotides (NPDs) and the nucleotide sequence-dependent structure of DNA. We make use of the random forest algorithm to flexibly exploit both types of information. Results in this study show that both the structural method and the NPD method can be valuable for the prediction of TFBSs. Moreover, their predictive values seem to be complementary, even to the widely used position weight matrix (PWM) method. This led us to combine all three methods. Results obtained for five eukaryotic TFs with different DNA-binding domains show that our method improves classification accuracy for all five eukaryotic TFs compared with other approaches. Additionally, we contrast the results of seven smaller prokaryotic sets with high-quality data and show that with the use of high-quality data we can significantly improve prediction performance. Models developed in this study can be of great use for gaining insight into the mechanisms of TF binding

XIPE: the X-ray Imaging Polarimetry Explorer

X-ray polarimetry, sometimes alone, and sometimes coupled to spectral and temporal variability measurements and to imaging, allows a wealth of physical phenomena in astrophysics to be studied. X-ray polarimetry investigates the acceleration process, for example, including those typical of magnetic reconnection in solar flares, but also emission in the strong magnetic fields of neutron stars and white dwarfs. It detects scattering in asymmetric structures such as accretion disks and columns, and in the so-called molecular torus and ionization cones. In addition, it allows fundamental physics in regimes of gravity and of magnetic field intensity not accessible to experiments on the Earth to be probed. Finally, models that describe fundamental interactions (e.g. quantum gravity and the extension of the Standard Model) can be tested. We describe in this paper the X-ray Imaging Polarimetry Explorer (XIPE), proposed in June 2012 to the first ESA call for a small mission with a launch in 2017 but not selected. XIPE is composed of two out of the three existing JET-X telescopes with two Gas Pixel Detectors (GPD) filled with a He-DME mixture at their focus and two additional GPDs filled with pressurized Ar-DME facing the sun. The Minimum Detectable Polarization is 14 % at 1 mCrab in 10E5 s (2-10 keV) and 0.6 % for an X10 class flare. The Half Energy Width, measured at PANTER X-ray test facility (MPE, Germany) with JET-X optics is 24 arcsec. XIPE takes advantage of a low-earth equatorial orbit with Malindi as down-link station and of a Mission Operation Center (MOC) at INPE (Brazil).Comment: 49 pages, 14 figures, 6 tables. Paper published in Experimental Astronomy http://link.springer.com/journal/1068

Estrogen receptor transcription and transactivation: Basic aspects of estrogen action

Estrogen signaling has turned out to be much more complex and exciting than previously thought; the paradigm shift in our understanding of estrogen action came in 1996, when the presence of a new estrogen receptor (ER), ERβ, was reported. An intricate interplay between the classical ERα and the novel ERβ is of paramount importance for the final biological effect of estrogen in different target cells

Abstracts from the 8th International Conference on cGMP Generators, Effectors and Therapeutic Implications

This work was supported by a restricted research grant of Bayer AG

Ophthalmomyiasis in Hawaii.

Ophthalmomyiasis is the infestation of the eye by fly larvae. Commonly caused by Oestrus ovis, a female sheep botfly will accidentally deposit her larvae into a human eye, resulting in disease. Prompt recognition and treatment of this condition will improve patient care and reduce potential complications. We report a case of ophthalmomyiasis in a young man from Molokai who was infested while unloading a Christmas tree