Galactic and Extragalactic Samples of Supernova Remnants: How They Are Identified and What They Tell Us

Supernova remnants (SNRs) arise from the interaction between the ejecta of a supernova (SN) explosion and the surrounding circumstellar and interstellar medium. Some SNRs, mostly nearby SNRs, can be studied in great detail. However, to understand SNRs as a whole, large samples of SNRs must be assembled and studied. Here, we describe the radio, optical, and X-ray techniques which have been used to identify and characterize almost 300 Galactic SNRs and more than 1200 extragalactic SNRs. We then discuss which types of SNRs are being found and which are not. We examine the degree to which the luminosity functions, surface-brightness distributions and multi-wavelength comparisons of the samples can be interpreted to determine the class properties of SNRs and describe efforts to establish the type of SN explosion associated with a SNR. We conclude that in order to better understand the class properties of SNRs, it is more important to study (and obtain additional data on) the SNRs in galaxies with extant samples at multiple wavelength bands than it is to obtain samples of SNRs in other galaxiesComment: Final 2016 draft of a chapter in "Handbook of Supernovae" edited by Athem W. Alsabti and Paul Murdin. Final version available at https://doi.org/10.1007/978-3-319-20794-0_90-

A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale

In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is however critical both for basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brain-wide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brain-wide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open access data repository; compatibility with existing resources, and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget.Comment: 41 page

Algorithms for differential splicing detection using exon arrays: a comparative assessment

Background: The analysis of differential splicing (DS) is crucial for understanding physiological processes in cells and organs. In particular, aberrant transcripts are known to be involved in various diseases including cancer. A widely used technique for studying DS are exon arrays. Over the last decade a variety of algorithms for the detection of DS events from exon arrays has been developed. However, no comprehensive, comparative evaluation including sensitivity to the most important data features has been conducted so far. To this end, we created multiple data sets based on simulated data to assess strengths and weaknesses of seven published methods as well as a newly developed method, KLAS. Additionally, we evaluated all methods on two cancer data sets that comprised RT-PCR validated results. Results: Our studies indicated ARH as the most robust methods when integrating the results over all scenarios and data sets. Nevertheless, special cases or requirements favor other methods. While FIRMA was highly sensitive according to experimental data, SplicingCompass, MIDAS and ANOSVA showed high specificity throughout the scenarios. On experimental data ARH, FIRMA, MIDAS, and KLAS performed best. Conclusions: Each method shows different characteristics regarding sensitivity, specificity, interference to certain data settings and robustness over multiple data sets. While some methods can be considered as generally good choices over all data sets and scenarios, other methods show heterogeneous prediction quality on the different data sets. The adequate method has to be chosen carefully and with a defined study aim in mind

Prioritization of Epilepsy Associated Candidate Genes by Convergent Analysis

Epilepsy is a severe neurological disorder affecting a large number of individuals, yet the underlying genetic risk factors for epilepsy remain unclear. Recent studies have revealed several recurrent copy number variations (CNVs) that are more likely to be associated with epilepsy. The responsible gene(s) within these regions have yet to be definitively linked to the disorder, and the implications of their interactions are not fully understood. Identification of these genes may contribute to a better pathological understanding of epilepsy, and serve to implicate novel therapeutic targets for further research.In this study, we examined genes within heterozygous deletion regions identified in a recent large-scale study, encompassing a diverse spectrum of epileptic syndromes. By integrating additional protein-protein interaction data, we constructed subnetworks for these CNV-region genes and also those previously studied for epilepsy. We observed 20 genes common to both networks, primarily concentrated within a small molecular network populated by GABA receptor, BDNF/MAPK signaling, and estrogen receptor genes. From among the hundreds of genes in the initial networks, these were designated by convergent evidence for their likely association with epilepsy. Importantly, the identified molecular network was found to contain complex interrelationships, providing further insight into epilepsy's underlying pathology. We further performed pathway enrichment and crosstalk analysis and revealed a functional map which indicates the significant enrichment of closely related neurological, immune, and kinase regulatory pathways.The convergent framework we proposed here provides a unique and powerful approach to screening and identifying promising disease genes out of typically hundreds to thousands of genes in disease-related CNV-regions. Our network and pathway analysis provides important implications for the underlying molecular mechanisms for epilepsy. The strategy can be applied for the study of other complex diseases

Splice variants as novel targets in pancreatic ductal adenocarcinoma

The study was funded by the MolDiagPaCa European Union Framework Programme and CR-UK Programme grant A12008 from CR-UK (C. Chelala, T. Crnogorac-Jurcevic, and N.R. Lemoine). Italian Cancer Genome Project – Ministry of University [FIRB RBAP10AHJB]; Associazione Italiana Ricerca Cancro [grant number: 12182]; FP7 European Community Grant Cam-Pac [no: 602783]; Italian Ministry of Health [FIMPCUP_J33G13000210001]. The funders were not involved in the design of the study, collection, analysis, and interpretation of data and in writing of the manuscript. We thank Tracy Chaplin-Perkins for help with running the Affymetrix experiments

Ectomycorrhizal fungal communities of native and non-native Pinus and Quercus species in a common garden of 35-year-old trees

Non-native tree species have been widely planted or have become naturalized in most forested landscapes. It is not clear if native trees species collectively differ in ectomycorrhizal fungal (EMF) diversity and communities from that of non-native tree species. Alternatively, EMF species community similarity may be more determined by host plant phylogeny than by whether the plant is native or non-native. We examined these unknowns by comparing two genera, native and non-native Quercus robur and Quercus rubra and native and non-native Pinus sylvestris and Pinus nigra in a 35-year-old common garden in Poland. Using molecular and morphological approaches, we identified EMF species from ectomycorrhizal root tips and sporocarps collected in the monoculture tree plots. A total of 69 EMF species were found, with 38 species collected only as sporocarps, 18 only as ectomycorrhizas, and 13 both as ectomycorrhizas and sporocarps. The EMF species observed were all native and commonly associated with a Holarctic range in distribution. We found that native Q. robur had ca. 120% higher total EMF species richness than the non-native Q. rubra, while native P. sylvestris had ca. 25% lower total EMF species richness than non-native P. nigra. Thus, across genera, there was no evidence that native species have higher EMF species diversity than exotic species. In addition, we found a higher similarity in EMF communities between the two Pinus species than between the two Quercus species. These results support the naturalization of non-native trees by means of mutualistic associations with cosmopolitan and novel fungi