Effects of beet western yellows virus on growth and yield of oilseed rape (Brassica napus )

Field trials were undertaken in Suffolk in commercial crops of autumn-sown oilseed rape cv. Capricorn during 1993/94, cv. Apex in 1994/95. Plots were artificially infected with beet western yellows virus (BWYV) using viruliferous Myzus persicae, giving 73 to 94% infection. Control plots had natural infection ranging from 0 to 17·8%. Destructive plant samples were taken from each of the infected and control plots throughout the seasons for growth analyses, and final yields were measured on 44 m2 areas combine harvested from each plot. The seed yields of infected plots were 26 and 11% lower than control plots in 1994 and 1995 respectively (P&lt;0·001).Harvested seed yields were shown to be inversely proportional to the area of the plot that was inoculated with BWYV. Infection significantly lowered the oil content in 1995 from 47·9 to 46·8% (P&lt;0·001), and increased glucosinolate levels from 16·12 to 18·37 μmol/g (P&lt;0·01). BWYV caused a significant reduction in plant height and in numbers of primary branches in the 1993/94 trial and had an effect on the dry weight of the leaves, stalks, racemes and pods at some sample dates in both seasons. Virus-testing of infected plants showed that BWYV was present in the pod wall, the septum and seed coat; two of 78 embryo samples also contained virus. It was concluded that BWYV can cause significant yield losses in those years in which there is a high incidence of virus in the overwintered crops.</jats:p

First results of the new n_TOF spallation target commissioning

The Neutron Time of Flight facility n_TOF located at CERN started to take data in 2001 . Due to an increase of radioactivity released in the cooling water the experiment was stopped by end of 2004 . In 2008 a new spallation target has been installed . In 2009 the collaboration has performed the full commissioning of the facility, consisting in the determination of the fluence, the beam profile, and the energy resolution of the neutron beam. After a brief description of the new target assembly, very preliminary results concerning the shape of the neutron fluence and its absolute value will be given. Measurements of the neutron beam profile will also be shown.Postprint (published version

combining first-principles with deep neural networks

JP acknowledges PhD grant SFRD/BD14610472019, Fundação para a Ciência e Tecnologia (FCT).Hybrid modeling combining First-Principles with machine learning is becoming a pivotal methodology for Biopharma 4.0 enactment. Chinese Hamster Ovary (CHO) cells, being the workhorse for industrial glycoproteins production, have been the object of several hybrid modeling studies. Most previous studies pursued a shallow hybrid modeling approach based on threelayered Feedforward Neural Networks (FFNNs) combined with macroscopic material balance equations. Only recently, the hybrid modeling field is incorporating deep learning into its framework with significant gains in descriptive and predictive power.publishersversionpublishe

Neutron beam imaging with micromegas detectors in combination with neutron time-of-flight at the n_tof facility at CERN

A bulk micromegas detector with the anode segmented in 2 orthogonal directions and equipped with a neutron/charged particle converter is employed at the neutron time-of-light (nTOF) facility at CERN to determine the incident neutron beam profile and beam interception factor as a function of the neutron energy determined by the time of flight. Discrepancies between experimental results and simulations in the values of the beam interception factor range up to 12 % and are to be ascribed to a defect in the mesh of the bulk. Nevertheless the detector proved to be really useful for checking the alignment of the neutron beam optics of the facility. Measurements with a new pixelized bulk detector for the determination of the beam interception factor are forseen before the end of 2012Postprint (published version

Reachability in High Treewidth Graphs

Reachability is the problem of deciding whether there is a path from one vertex to the other in the graph. Standard graph traversal algorithms such as DFS and BFS take linear time to decide reachability however their space complexity is also linear. On the other hand, Savitch's algorithm takes quasipolynomial time although the space bound is $O(\log^2 n)$. Here, we study space efficient algorithms for deciding reachability that runs simultaneously in polynomial time. In this paper, we show that given an $n$ vertex directed graph of treewidth $w$ along with its tree decomposition, there exists an algorithm running in polynomial time and $O(w\log n)$ space, that solves reachability in the graph

FSK-based Simultaneous Wireless Information and Power Transfer in Inductively Coupled Resonant Circuits Exploiting Frequency Splitting

Inductively coupled resonant circuits are affected by the so-called frequency splitting phenomenon at short distances. In the area of power electronics, tracking of one of the peak frequencies is state-of-the-art. In the data transmission community, however, the frequency splitting effect is often ignored. Particularly, modulation schemes have not yet been adapted to the bifurcation phenomenon. We argue that binary frequency shift keying (2-ary FSK) is a low-cost modulation scheme which well matches the double-peak voltage transfer function $H(s)$, particularly when the quality factor $Q$ is large, whereas most other modulation schemes suffer from the small bandwidths of the peaks. Additionally we show that a rectified version of 2-ary FSK, coined rectified FSK (RFSK), is even more attractive from output power and implementation points of view. Analytical and numerical contributions include the efficiency factor, the impulse response, and the bit error performance. A low-cost incoherent receiver is proposed. Theoretical examinations are supported by an experimental prototype

Heterogeneous large datasets integration using bayesian factor regression

Two key challenges in modern statistical applications are the large amount of information recorded per individual, and that such data are often not collected all at once but in batches. These batch effects can be complex, causing distortions in both mean and variance. We propose a novel sparse latent factor regression model to integrate such heterogeneous data. The model provides a tool for data exploration via dimensionality reduction and sparse low-rank covariance estimation while correcting for a range of batch effects. We study the use of several sparse priors (local and non-local) to learn the dimension of the latent factors. We provide a flexible methodology for sparse factor regression which is not limited to data with batch effects. Our model is fitted in a deterministic fashion by means of an EM algorithm for which we derive closed-form updates, contributing a novel scalable algorithm for non-local priors of interest beyond the immediate scope of this paper. We present several examples, with a focus on bioinformatics applications. Our results show an increase in the accuracy of the dimensionality reduction, with non-local priors substantially improving the reconstruction of factor cardinality. The results of our analyses illustrate how failing to properly account for batch effects can result in unreliable inference. Our model provides a novel approach to latent factor regression that balances sparsity with sensitivity in scenarios both with and without batch effects and is highly computationally efficient