Productivity of reed (Phragmites australis Trin. ex Steud.) in continental-arid NW China in relation to soil, groundwater, and land-use

Reed (Phragmites australis Trin. ex Steud.) is a cosmopolitan plant species which can build up large stands in wetlands, floodplains, and on sites where groundwater is available. Phragmites australis provides many ecosystem services, such as the production of raw material (e.g. house construction or organic fuel). In the desert regions of Central Asia, reed occurs along river, e.g. the Tarim, Syr Darya, Amu Darya, and serves as fodder plant and raw material for construction and paper production. In those arid regions, reed occurs on submerged sites as well as non-flooded sites in a wide variety of phenotypes, ranging from so-called „giant reed“ (2-4 m high) to dwarf-like thorny reed not exceeding 40 cm stem length. We investigated the net primary production of the different phenotypes and their distribution with regard to soil and groundwater salt content and regarding grazing. The phenotypes were characterized through stem length, stem diameter, number of leaves per stem length, leaf weight ratio, leaf length, and leaf width. The net primary production reached 6,004 kg/ha·a on a non-grazed site, which is submerged for one month in late summer. The depth of the closed capillary fringe before onset of the flood was 2.2 m. The electric conductivity at the closed capillary fringe (determined from a water saturated soil extract) was 2 mS/cm. Stem length and stem diameter did not decrease with increasing soil and groundwater salt content, as expected. Conversely, stem length and stem diameter decreased and leaf weight ratio increased with increasing grazing intensity. Thus, grazing turned reed into dwarf-like thorny phenotypes. Non-grazed reed stands are the most productive ecosystems of the riparian vegetation at the Tarim and have a high potential to be used as raw material plant. We conclude that biomass harvesting could be an alternative to grazing with regard to sustainable land use

Apocynum venetum L. and Apocynum pictum Schrenk (Apocynaceae) as multi-functional and multi-service plant species in Central Asia: a review on biology, ecology, and utilization

During the second half of the 20th century cotton was strongly promoted along the rivers of Central Asia. The irrigation agriculture resulted in wide spread soil salinization and severe water shortages within the river systems. Most prominent example is the desiccation of the Aral Sea. The natural vegetation along the rivers of Central Asia is adapted to periods of water shortage, is very productive, and contains plant species with valuable utilization opportunities. We reviewed the literature about Apocynum venetumL. and A. pictum Schrenk, two plant species of those riparian ecosystems, which are used as fibre and medicinal plants. A. venetum and A. pictum yield fibres, which can be used as textiles, though the fibres best are blended with cotton and/or chemical fibres. Though, the fibre extraction process needs more research attention. Furthermore, the literature shows that Apocynum leafs are used to produce antihypertonic tea and medicine. Both species grow under the arid climate of Central Asia without irrigation, because they exploit groundwater. Furthermore, both species can withstand higher soil salinization levels than cotton. Both species can be used and provide an income to local people under conditions, which are unfavourable to grow crops under irrigation. Such conditions are unreliable water supply for irrigation systems and/or saline soils

Confidence interval estimation for the changepoint of treatment stratification in the presence of a qualitative covariate-treatment interaction

The goal in stratified medicine is to administer the \textquotedblbest\textquotedbl treatment to a patient. Not all patients might benefit from the same treatment; the choice of best treatment can depend on certain patient characteristics. In this article, it is assumed that a time-to-event outcome is considered as a patient-relevant outcome and a qualitative interaction between a continuous covariate and treatment exists, ie,~that patients with different values of one specific covariate should be treated differently. We suggest and investigate different methods for confidence interval estimation for the covariate value, where the treatment recommendation should be changed based on data collected in a randomized clinical trial. An adaptation of Fieller's theorem, the delta method, and different bootstrap approaches (normal, percentile-based, wild bootstrap) are investigated and compared in a simulation study. Extensions to multivariable problems are presented and evaluated. We observed appropriate confidence interval coverage following Fieller's theorem irrespective of sample size but at the cost of very wide or even infinite confidence intervals. The delta method and the wild bootstrap approach provided the smallest intervals but inadequate coverage for small to moderate event numbers, also depending on the location of the true changepoint. For the percentile-based bootstrap, wide intervals were observed, and it was slightly conservative regarding coverage, whereas the normal bootstrap did not provide acceptable results for many scenarios. The described methods were also applied to data from a randomized clinical trial comparing two treatments for patients with symptomatic, severe carotid artery stenosis, considering patient's age as predictive marker

Deep Convolutional Neural Networks for Histological Image Analysis in Gastric Carcinoma Whole Slide Images

Introduction/ Background In this paper, histopathological whole slide images of gastric carcinoma are analyzed using deep learning methods. A convolutional neural network architecture is proposed for two classification applications in H&E stained tissue images, namely, cancer classification based on immunohistochemistry (IHC) into classes Her2/neu+ tumor, Her2/neu- tumor and non-tumor, and necrosis detection based on existence of necrosis into classes necrotic and non-necrotic. The studies in [1] and [2] explored computer-aided classification using graphbased methods and necrosis detection by textural approach respectively, which are extended using deep convolutional neural networks. Performance is quantitatively compared with established handcrafted image features, namely Haralick GLCM, Gabor filter-banks, LBP histograms, Gray histograms, RGB histograms and HSV histograms followed by classification by random forests, another well-known machine learning algorithm. Aims Convolutional neural networks (CNN) have recently gained tremendous attention in general image analysis [3-5]. There has also been an emergence of deep learning in digital histopathology for diverse classification and detection problems [6-8]. The prime motivation behind this work is that no previous study has explored deep learning for the specified goals in gastric cancer WSI. Automated cancer classification can assist pathologists in computer-aided diagnosis in H&E stained WSI without the requirement of IHC staining, thereby reducing preparation and inspection times, and decreasing inter- and intra-observer variability. Necrosis detection can play an important role in prognosis, as larger necrotic areas indicate a smaller chance of survival and vice-versa. Moreover, most deep learning studies have used smaller image sizes mainly due to memory restrictions of GPU, however, we consider larger regions in order to preserve context i.e. neighborhood information and tissue architecture at higher magnification. Further, this method is independent of nuclei segmentation, hence its performance is not limited by segmentation performance as in [1] (evaluation details in [9]). Methods Firstly, standard data augmentation techniques are applied on the available gastric cancer WSI dataset and thousands of images of size 512x512 are generated. Different CNN architectures are empirically studied to observe the behavior of variation in model characteristics (network depth, layer properties, training parameters, etc.) by training them from scratch on a representative subset of whole data for cancer classification. One of these is the Imagenet model [4], however it doesn’t perform desirably on the representative dataset. The self-designed CNN architecture with best classification rates is selected. Later, the proposed CNN is also applied for necrosis detection. Performance is compared with state of the art methods using handcrafted features and random forests. For evaluation, randomized three-fold stratified shuffle split and leave-one-patient-out cross validations are used. Results Conclusion: A self-designed CNN architecture is proposed for image analysis (cancer classification based on IHC and necrosis detection) in H&E stained WSI of gastric cancer. Quantitative evaluation shows that deep learning methods mostly compare favorably to state of the art methods, especially for necrosis detection. In future the aim is to expand the current WSI dataset and to improve the CNN architecture for optimal performance

Tissue-specific transcriptome analysis reveals candidate genes for terpenoid and phenylpropanoid metabolism in the medicinal plant ferula assafoetida

© 2019 ABRF. Methionine oxidation plays a critical role in many processes of biologic and biomedical importance, including cellular redox responses and stability of protein pharmaceuticals. Bottom-up methods for analysis of methionine oxidation can suffer from incomplete sequence coverage, as well as an inability to readily detect correlated oxidation between 2 or more methionines. However, the methodology for quantifying protein oxidation in top-down analyses is lacking. Previous work has shown that electron transfer dissociation (ETD)–based tandem mass spectrometry (MS/MS) fragmentation offers accurate and precise quantification of amino acid oxidation in peptides, even in complex samples. However, the ability of ETD-based MS/MS fragmentation to accurately quantify amino acid oxidation of proteins in a top-down manner has not been reported. Using apomyoglobin and calmodulin as model proteins, we partially converted methionines into methionine sulfoxide by incubation in H2O2. Using top-down ETD-based fragmentation, we quantified the amount of oxidation of various ETD product ions and compared the quantified values with those from traditional bottom-up analysis. We find that overall quantification of methionine oxidation by top-down MS/MS ranges from good agreement with traditional bottom-up methods to vast differences between the 2 techniques, including missing oxidized product ions and large differences in measured oxidation quantities. Care must be taken in transitioning ETD-based quantitation of oxidation from the peptide level to the intact protein level

Iterative algorithm versus analytic solutions of the parametrically driven dissipative quantum harmonic oscillator

We consider the Brownian motion of a quantum mechanical particle in a one-dimensional parabolic potential with periodically modulated curvature under the influence of a thermal heat bath. Analytic expressions for the time-dependent position and momentum variances are compared with results of an iterative algorithm, the so-called quasiadiabatic propagator path integral algorithm (QUAPI). We obtain good agreement over an extended range of parameters for this spatially continuous quantum system. These findings indicate the reliability of the algorithm also in cases for which analytic results may not be available a priori.Comment: 15 pages including 11 figures, one reference added, minor typos correcte

Immunohistochemical demonstration of cyclooxygenase-2 (COX-2) and prostaglandin receptors EP2 and FP expression in the bovine intercaruncular uterine wall around term

During parturition, uterine-derived prostaglandins (PG) play an outstanding role regarding the functional elimination of the corpus luteum and the promotion of uterine contraction. The rate-limiting enzyme cyclooxygenase-2 (COX-2), highly regulated in a cell-type and localization specific manner throughout pregnancy, is involved in uterine prostanoid production. Prostaglandins exert their effects via G-protein-coupled receptors. Distribution and cellular localization of these receptors are decisive factors for prostaglandin-mediated actions. Since both COX-2 and PG receptors have only been assessed during pregnancy in the cow, these parameters were localized immunohistochemically near term to evaluate their specific role at parturition. Thus, during two periods, segments of the intercaruncular uterine wall were collected from cows at slaughter being eight and nine months pregnant, from cattle during caesarean section, and after spontaneous calving. Results reveal that COX-2 was mainly localized in the cytoplasm of surface epithelial cells with a high expression in animals with induced parturition. The enzyme could also be found in lower concentrations within the glandular epithelium without any effect of gestational time or labour. In contrast to relaxant prostaglandin E receptor type 2 (EP2), not showing any change in all tissue layers observed, contractile prostaglandin F(2alpha) receptor (FP) was modulated during the peripartal period revealing a peak expression in animals with induced parturition. FP was localized in surface and glandular epithelial cells as well as in endometrial stroma and myometrial smooth muscle cells. Our study indicates that labour and induction of parturition may have an effect on amounts of immunohistochemically detectable COX-2 and FP. EP2 remains rather unchanged during the peripartal period. COX-2 and FP thus contribute via changes in amount and distribution to mechanisms associated with parturition