Diffusion Tensor Imaging and Tractography in Brown-Sequard Syndrome

This report illustrates the utility of DTI and DTT in delineating regions of cord injury in two patients with traumatic Brown-Sequard syndrome. Our results indicate that DTI provides clinically relevant information that supplements conventional MR imaging for patients with acute SCI

Epidermal Neural Crest Stem Cell (EPI-NCSC)—Mediated Recovery of Sensory Function in a Mouse Model of Spinal Cord Injury

Here we show that epidermal neural crest stem cell (EPI-NCSC) transplants in the contused spinal cord caused a 24% improvement in sensory connectivity and a substantial recovery of touch perception. Furthermore we present a novel method for the ex vivo expansion of EPI-NCSC into millions of stem cells that takes advantage of the migratory ability of neural crest stem cells and is based on a new culture medium and the use of microcarriers. Functional improvement was shown by two independent methods, spinal somatosensory evoked potentials (SpSEP) and the Semmes-Weinstein touch test. Subsets of transplanted cells differentiated into myelinating oligodendrocytes. Unilateral injections of EPI-NCSC into the lesion of midline contused mouse spinal cords elicited bilateral improvements. Intraspinal EPI-NCSC did not migrate laterally in the spinal cord or invade the spinal roots and dorsal root ganglia, thus implicating diffusible factors. EPI-NCSC expressed neurotrophic factors, angiogenic factors, and metalloproteases. The strength of EPI-NCSC thus is that they can exert a combination of pertinent functions in the contused spinal cord, including cell replacement, neuroprotection, angiogenesis and modulation of scar formation. EPI-NCSC are uniquely qualified for cell-based therapy in spinal cord injury, as neural crest cells and neural tube stem cells share a higher order stem cell and are thus ontologically closely related

Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals

Genome-wide association studies (GWASs) have identified thousands of cancer risk loci revealing many risk regions shared across multiple cancers. Characterizing the cross-cancer shared genetic basis can increase our understanding of global mechanisms of cancer development. In this study, we collected GWAS summary statistics based on up to 375,468 cancer cases and 530,521 controls for fourteen types of cancer, including breast (overall, estrogen receptor [ER]-positive, and ER-negative), colorectal, endometrial, esophageal, glioma, head/neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancer, to characterize the shared genetic basis of cancer risk. We identified thirteen pairs of cancers with statistically significant local genetic correlations across eight distinct genomic regions. Specifically, the 5p15.33 region, harboring the TERT and CLPTM1L genes, showed statistically significant local genetic correlations for multiple cancer pairs. We conducted a cross-cancer fine-mapping of the 5p15.33 region based on eight cancers that showed genome-wide significant associations in this region (ER-negative breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, and prostate cancer). We used an iterative analysis pipeline implementing a subset-based meta-analysis approach based on cancer-specific conditional analyses and identified ten independent cross-cancer associations within this region. For each signal, we conducted cross-cancer fine-mapping to prioritize the most plausible causal variants. Our findings provide a more in-depth understanding of the shared inherited basis across human cancers and expand our knowledge of the 5p15.33 region in carcinogenesis

Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals.

Genome-wide association studies (GWASs) have identified thousands of cancer risk loci revealing many risk regions shared across multiple cancers. Characterizing the cross-cancer shared genetic basis can increase our understanding of global mechanisms of cancer development. In this study, we collected GWAS summary statistics based on up to 375,468 cancer cases and 530,521 controls for fourteen types of cancer, including breast (overall, estrogen receptor [ER]-positive, and ER-negative), colorectal, endometrial, esophageal, glioma, head/neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancer, to characterize the shared genetic basis of cancer risk. We identified thirteen pairs of cancers with statistically significant local genetic correlations across eight distinct genomic regions. Specifically, the 5p15.33 region, harboring the TERT and CLPTM1L genes, showed statistically significant local genetic correlations for multiple cancer pairs. We conducted a cross-cancer fine-mapping of the 5p15.33 region based on eight cancers that showed genome-wide significant associations in this region (ER-negative breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, and prostate cancer). We used an iterative analysis pipeline implementing a subset-based meta-analysis approach based on cancer-specific conditional analyses and identified ten independent cross-cancer associations within this region. For each signal, we conducted cross-cancer fine-mapping to prioritize the most plausible causal variants. Our findings provide a more in-depth understanding of the shared inherited basis across human cancers and expand our knowledge of the 5p15.33 region in carcinogenesis

Search for triboson W±W±W∓ production in pp collisions at √s=8 TeV with the ATLAS detector

This paper reports a search for triboson $W^{\pm}W^{\pm}W^{\mp}$ production in two decay channels ($W^{\pm}W^{\pm}W^{\mp}\rightarrow \ell^{\pm}\nu\ell^{\pm}\nu\ell^{\mp}\nu$ and $W^{\pm}W^{\pm}W^{\mp}\rightarrow \ell^{\pm}\nu\ell^{\pm}\nu{}jj$ with $\ell=e, \mu$) in proton-proton collision data corresponding to an integrated luminosity of 20.3 fb$^{-1}$ at a centre-of-mass energy of 8 TeV with the ATLAS detector at the Large Hadron Collider. Events with exactly three charged leptons, or two leptons with the same electric charge in association with two jets, are selected. The total number of events observed in data is consistent with the Standard Model (SM) predictions. The observed 95 % confidence level upper limit on the SM $W^{\pm}W^{\pm}W^{\mp}$ production cross section is found to be 730 fb with an expected limit of 560 fb in the absence of SM $W^{\pm}W^{\pm}W^{\mp}$ production. Limits are also set on $WWWW$ anomalous quartic gauge couplings.Comment: Comments: 39 pages in total, author list starting page 23, 5 figures, 7 tables, submitted to European Physics Journal C, All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/STDM-2015-07

Performance of algorithms that reconstruct missing transverse momentum in √s= 8 TeV proton-proton collisions in the ATLAS detector

The reconstruction and calibration algorithms used to calculate missing transverse momentum (EmissT ) with the ATLAS detector exploit energy deposits in the calorimeter and tracks reconstructed in the inner detector as well as the muon spectrometer. Various strategies are used to suppress effects arising from additional proton–proton interactions, called pileup, concurrent with the hard-scatter processes. Tracking information is used to distinguish contributions from the pileup interactions using their vertex separation along the beam axis. The performance of the EmissT reconstruction algorithms, especially with respect to the amount of pileup, is evaluated using data collected in proton–proton collisions at a centre-of-mass energy of 8 TeV during 2012, and results are shown for a data sample corresponding to an integrated luminosity of 20.3fb−1. The simulation and modelling of EmissT in events containing a Z boson decaying to two charged leptons (electrons or muons) or a W boson decaying to a charged lepton and a neutrino are compared to data. The acceptance for different event topologies, with and without high transverse momentum neutrinos, is shown for a range of threshold criteria for EmissT , and estimates of the systematic uncertainties in the EmissT measurements are presented.ATLAS Collaboration, for complete list of authors see dx.doi.org/10.1140/epjc/s10052-017-4780-2Funding: We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently.We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, UK; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, FP7, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d’Avenir Labex and Idex, ANR, Région Auvergne and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF; BSF, GIF and Minerva, Israel; BRF, Norway; Generalitat de Catalunya, Generalitat Valenciana, Spain; the Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [58].</p