Tumor necrosis factor alpha antagonist drugs and leishmaniasis in Europe.

Leishmaniasis is endemic in Europe and the prevalence of latent infection in the Mediterranean region is high. Reports describing opportunistic leishmaniasis in European patients treated with tumor necrosis factor (TNF) alpha antagonist drugs are rapidly accumulating. For other granulomatous infections, risk of opportunistic disease varies by mode of TNF-alpha antagonism. This study explores whether this may also be the case for leishmaniasis. We ascertained the relative frequency of exposure to different TNF antagonist drugs among published cases of opportunistic leishmaniasis in Europe and compared this with the prescription of these drugs in Europe. We found that risk of opportunistic leishmaniasis is higher in patients receiving anti-TNF monoclonal antibodies (infliximab or adalimumab) compared with patients treated with the TNF-receptor construct etanercept. Clinicians may want to consider these observations, which suggest that etanercept should be favoured over anti-TNF monoclonal antibodies in individuals living in or visiting areas endemic for leishmaniasis until evidence from prospective research is available. A European adverse event reporting system is required to identify rare opportunistic infections associated with immunosuppressive and immunomodulatory biotherapies

Multiple transcripts regulate glucose-triggered mRNA decay of the lactate transporter JEN1 from Saccharomyces cerevisiae

The Saccharomyces cerevisiae JEN1 gene encoding the lactate transporter undergoes strong catabolic repression at both transcriptional and post-transcriptional levels. JEN1 mRNA decay is greatly accelerated upon the addition of a pulse of glucose, fructose or mannose to induced cell cultures. Mapping of the 5´UTRs and 3´UTRs of JEN1 transcripts revealed multiple transcription start-sites located at position -51, +391 or +972, depending on the cell culture conditions. The presence of the JEN1(+391) transcript correlated with rapid glucose-triggered mRNA degradation of the JEN1(-51) transcript, whereas when the small transcript started at position +972, the JEN1(-51) mRNA turnover rate was unaffected. Overexpressed JEN1(+391) transcript accelerated JEN1(-51) mRNA decay in all conditions tested but was not translated. We propose that the JEN1(+391) transcript may have a ‘‘sensor-like’’ function, regulating glucose-triggered degradation of JEN1(-51) protein-coding mRNA.Fundação para a Ciência e a Tecnologia (FCT) - Programa Operacional "Ciência, Tecnologia, Inovação (POCTI) - POCTI/BIO/38106/2001 (Eixo 2, Medida 2.3, QCAIII-FEDER), BD/15737/98, SFRH/BPD/9432/2002. Deutsche Forschungsgemeinschaft (SFB 579)

Robust vetoes for gravitational-wave burst triggers using known instrumental couplings

The search for signatures of transient, unmodelled gravitational-wave (GW) bursts in the data of ground-based interferometric detectors typically uses `excess-power' search methods. One of the most challenging problems in the burst-data-analysis is to distinguish between actual GW bursts and spurious noise transients that trigger the detection algorithms. In this paper, we present a unique and robust strategy to `veto' the instrumental glitches. This method makes use of the phenomenological understanding of the coupling of different detector sub-systems to the main detector output. The main idea behind this method is that the noise at the detector output (channel H) can be projected into two orthogonal directions in the Fourier space -- along, and orthogonal to, the direction in which the noise in an instrumental channel X would couple into H. If a noise transient in the detector output originates from channel X, it leaves the statistics of the noise-component of H orthogonal to X unchanged, which can be verified by a statistical hypothesis testing. This strategy is demonstrated by doing software injections in simulated Gaussian noise. We also formulate a less-rigorous, but computationally inexpensive alternative to the above method. Here, the parameters of the triggers in channel X are compared to the parameters of the triggers in channel H to see whether a trigger in channel H can be `explained' by a trigger in channel X and the measured transfer function.Comment: 14 Pages, 8 Figures, To appear in Class. Quantum Gra

The crystal structure of Nep1 reveals an extended SPOUT-class methyltransferase fold and a pre-organized SAM-binding site

Ribosome biogenesis in eukaryotes requires the participation of a large number of ribosome assembly factors. The highly conserved eukaryotic nucleolar protein Nep1 has an essential but unknown function in 18S rRNA processing and ribosome biogenesis. In Saccharomyces cerevisiae the malfunction of a temperature-sensitive Nep1 protein (nep1-1ts) was suppressed by the addition of S-adenosylmethionine (SAM). This suggests the participation of Nep1 in a methyltransferase reaction during ribosome biogenesis. In addition, yeast Nep1 binds to a 6-nt RNA-binding motif also found in 18S rRNA and facilitates the incorporation of ribosomal protein Rps19 during the formation of pre-ribosomes. Here, we present the X-ray structure of the Nep1 homolog from the archaebacterium Methanocaldococcus jannaschii in its free form (2.2 Å resolution) and bound to the S-adenosylmethionine analog S-adenosylhomocysteine (SAH, 2.15 Å resolution) and the antibiotic and general methyltransferase inhibitor sinefungin (2.25 Å resolution). The structure reveals a fold which is very similar to the conserved core fold of the SPOUT-class methyltransferases but contains a novel extension of this common core fold. SAH and sinefungin bind to Nep1 at a preformed binding site that is topologically equivalent to the cofactor-binding site in other SPOUT-class methyltransferases. Therefore, our structures together with previous genetic data suggest that Nep1 is a genuine rRNA methyltransferase

The crystal structure of Nep1 reveals an extended SPOUT-class methyltransferase fold and a pre-organized SAM-binding site

Ribosome biogenesis in eukaryotes requires the participation of a large number of ribosome assembly factors. The highly conserved eukaryotic nucleolar protein Nep1 has an essential but unknown function in 18S rRNA processing and ribosome biogenesis. In Saccharomyces cerevisiae the malfunction of a temperature-sensitive Nep1 protein (nep1-1ts) was suppressed by the addition of S-adenosylmethionine (SAM). This suggests the participation of Nep1 in a methyltransferase reaction during ribosome biogenesis. In addition, yeast Nep1 binds to a 6-nt RNA-binding motif also found in 18S rRNA and facilitates the incorporation of ribosomal protein Rps19 during the formation of pre-ribosomes. Here, we present the X-ray structure of the Nep1 homolog from the archaebacterium Methanocaldococcus jannaschii in its free form (2.2 Å resolution) and bound to the S-adenosylmethionine analog S-adenosylhomocysteine (SAH, 2.15 Å resolution) and the antibiotic and general methyltransferase inhibitor sinefungin (2.25 Å resolution). The structure reveals a fold which is very similar to the conserved core fold of the SPOUT-class methyltransferases but contains a novel extension of this common core fold. SAH and sinefungin bind to Nep1 at a preformed binding site that is topologically equivalent to the cofactor-binding site in other SPOUT-class methyltransferases. Therefore, our structures together with previous genetic data suggest that Nep1 is a genuine rRNA methyltransferase

Discovering universal statistical laws of complex networks

Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their generalisation power, which we identify with large structural variability and absence of constraints imposed by the construction scheme. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This allows, for instance, to infer global features from local ones using regression models trained on networks with high generalisation power. Our results confirm and extend previous findings regarding the synchronisation properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks with good approximation. Finally, we demonstrate on three different data sets (C. elegans' neuronal network, R. prowazekii's metabolic network, and a network of synonyms extracted from Roget's Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models

(Missing) Concept Discovery in Heterogeneous Information Networks

This article proposes a new approach to extract existing (or detect missing) concepts from a loosely integrated collection of information units by means of concept graph detection. Thereby a concept graph defines a concept by a quasi bipartite sub-graph of a bigger network with the members of the concept as the first vertex partition and their shared aspects as the second vertex partition. Once the concepts have been extracted they can be used to create higher level representations of the data. Concept graphs further allow the discovery of missing concepts, which could lead to new insights by connecting seemingly unrelated information units

An Algebraic Approach for Decoding Spread Codes

In this paper we study spread codes: a family of constant-dimension codes for random linear network coding. In other words, the codewords are full-rank matrices of size (k x n) with entries in a finite field F_q. Spread codes are a family of optimal codes with maximal minimum distance. We give a minimum-distance decoding algorithm which requires O((n-k)k^3) operations over an extension field F_{q^k}. Our algorithm is more efficient than the previous ones in the literature, when the dimension k of the codewords is small with respect to n. The decoding algorithm takes advantage of the algebraic structure of the code, and it uses original results on minors of a matrix and on the factorization of polynomials over finite fields

Asymptotic bounds for the sizes of constant dimension codes and an improved lower bound

We study asymptotic lower and upper bounds for the sizes of constant dimension codes with respect to the subspace or injection distance, which is used in random linear network coding. In this context we review known upper bounds and show relations between them. A slightly improved version of the so-called linkage construction is presented which is e.g. used to construct constant dimension codes with subspace distance $d=4$, dimension $k=3$ of the codewords for all field sizes $q$, and sufficiently large dimensions $v$ of the ambient space, that exceed the MRD bound, for codes containing a lifted MRD code, by Etzion and Silberstein.Comment: 30 pages, 3 table